NeuralNetwork and Intelligent Information Processing

人工智能的自然语言处理和信息检索方法概述人工智能（Artificial Intelligence，简称AI）是一门涉及计算机科学和工程学的跨学科科学，旨在研究和开发智能机器，使其能够模拟人类的思维过程并执行类似人类的任务。

人工智能的一个重要领域是自然语言处理（Natural Language Processing，简称NLP）和信息检索（Information Retrieval，简称IR），它们通过处理和分析自然语言数据，使计算机能够理解和生成自然语言。

本文将介绍人工智能中的自然语言处理和信息检索方法，并探讨其在各个领域中的应用。

自然语言处理自然语言处理是研究计算机和人类自然语言之间的相互作用的领域。

NLP旨在让计算机能够理解、分析和生成自然语言，包括语音识别、自动语音生成、机器翻译、信息抽取、文本分类等任务。

下面介绍几种常用的自然语言处理方法。

1. 词法分析（Lexical Analysis）：词法分析是将文本分解为单词、词汇和其他标记的过程。

常见的词法分析技术包括分词（Tokenization）、词性标注（Part-of-Speech Tagging）等。

2. 句法分析（Syntactic Parsing）：句法分析是分析句子结构的过程，将句子分解为组成成分和它们之间的关系。

常见的句法分析方法包括依存分析（Dependency Parsing）和短语结构分析（Phrase Structure Parsing）等。

3. 语义分析（Semantic Analysis）：语义分析旨在理解和表达文本的意思。

常见的语义分析方法包括命名实体识别（Named Entity Recognition）、实体关系抽取（Relation Extraction）、情感分析（Sentiment Analysis）等。

4. 信息抽取（Information Extraction）：信息抽取是从大量文本中抽取结构化信息的过程。

1956达特茅斯会议提出的概念1956年，达特茅斯会议（Dartmouth Conference）在美国新罕布什尔州达特茅斯学院（Dartmouth College）召开，聚集了一群领先的计算机科学家和人工智能研究者，共同探讨和交流计算机和人工智能领域的前沿问题。

在这次会议上，一系列重要的概念被提出并对计算机科学和人工智能领域的发展产生了深远的影响。

一、人工智能（Artificial Intelligence）达特茅斯会议是第一次正式提出“人工智能”（Artificial Intelligence）这一概念的重要场合。

人工智能是指利用计算机技术来模拟和实现人类智能的研究和应用领域。

与传统的计算机程序不同，人工智能系统可以从大量的数据中学习，具备感知、理解、推理、决策、自主行动等人类智能的基本能力。

二、机器学习（Machine Learning）在达特茅斯会议上，机器学习（Machine Learning）的概念也首次被提出。

机器学习是人工智能领域的一个重要分支，主要研究如何使计算机系统在无需明确编程的情况下自动学习和改进。

通过机器学习，计算机可以从数据中提取模式和规律，并利用这些知识来完成各种任务。

三、专家系统（Expert System）会议上还提出了专家系统（Expert System）的概念。

专家系统是一种基于知识库和推理机制的计算机程序，通过模拟人类专家的决策过程和知识判断能力，来解决特定的专业领域问题。

专家系统的出现为各个领域的决策和问题解决提供了新的途径。

四、自然语言处理（Natural Language Processing）自然语言处理（Natural Language Processing）也是达特茅斯会议上被提出的一个重要概念。

自然语言处理是人工智能领域涉及语言和计算机交互的一个子领域，研究如何使计算机能够理解、分析和处理人类自然语言。

该技术使得人与计算机之间的交流更加自然和高效。

人工智能研究进展史忠植智能信息处理开放实验室中国科学院计算技术研究所，北京 100080shizz@摘要：本文以第十届国际3W会议和第十七届国际人工智能联合会议为背景,介绍人工智能研究取得的进展。

特别对知识表示、概率推理、本体论、智能主体、机器学习等问题进行讨论。

关键词：知识表示概率推理本体论智能主体机器学习The Progress of Research on Artificial IntelligenceZhongzhi ShiIntelligent Information Processing Open LaboratoryInstitute of Computing Technology, Chinese Academy of Sciences, Beijing 100080shizz@Abstract：Based on the Tenth International Word Wide Web Conference and the Seventeenth International Joint Conference on Artificial Intelligence, paper will introduce the progress of research on Artificial Intelligence. Particularly, Knowledge Representation, Probabilistic Reasoning,Ontology, Intelligent Agent, Machine Learning etc. will be discussed in the paperKey Words: Knowledge Representation Probabilistic Reasoning Ontology Intelligent Agent Machine Learning1 概述由国际人工智能联合会（IJCAII）和美国人工智能学会（AAAI）主办的第17届国际人工智能联合会议（17th International Joint Conference on Artificial Intelligence）于2001年8月4至10日在美国华盛顿州西雅图召开。

毕业论文外文文献翻译译文题目:INTEGRATION OF MACHINERY外文资料翻译资料来源：文章名：INTEGRATION OF MACHINERY 《Digital Image Processing》书刊名：作者：Y. Torres J. J. Pavón I. Nieto and J. A.Rodríguez章节：2.4 INTEGRATION OF MACHINERYINTEGRATION OF MACHINERY （From ELECTRICAL AND MACHINERY INDUSTRY）ABSTRACT Machinery was the modern science and technology development inevitable resultthis article has summarized the integration of machinery technology basic outlineand the development background .Summarized the domestic and foreign integration ofmachinery technology present situation has analyzed the integration of machinerytechnology trend of development. Key word：integration of machinery ，technology，present situation ，productt，echnique of manufacture ，trend of development 0. Introduction modern science and technology unceasing development impelleddifferent discipline intersecting enormously with the seepage has caused the projectdomain technological revolution and the transformation .In mechanical engineeringdomain because the microelectronic technology and the computer technology rapiddevelopment and forms to the mechanical industry seepage the integration of machinerycaused the mechanical industry the technical structure the product organizationthe function and the constitution the production method and the management systemhas had the huge change caused the industrial production to enter into quottheintegration of machineryquot by quotthe machinery electrificationquot for the characteristicdevelopment phase. 1. Integration of machinery outline integration of machinery is refers in theorganization new owner function the power function in the information processingfunction and the control function introduces the electronic technology unifies thesystem the mechanism and the computerization design and the software whichconstitutes always to call. The integration of machinery development also has becomeone to have until now own system new discipline not only develops along with thescience and technology but also entrusts with the new content .But its basiccharacteristic may summarize is: The integration of machinery is embarks from thesystem viewpoint synthesis community technologies and so on utilization mechanicaltechnology microelectronic technology automatic control technology computertechnology information technology sensing observation and control technologyelectric power electronic technology connection technology information conversiontechnology as well as software programming technology according to the systemfunction goal and the optimized organization goal reasonable disposition and thelayout various functions unit in multi-purpose high grade redundant reliable inthe low energy consumption significance realize the specific function value andcauses the overall system optimization the systems engineering technology .From thisproduces functional system then becomes an integration of machinery systematic orthe integration of machinery product. Therefore quotintegration of machineryquot coveringquottechnologyquot and quotproductquot two aspects .Only is the integration of machinerytechnology is based on the above community technology organic fusion one kind ofcomprehensivetechnology but is not mechanical technical the microelectronictechnology as well as other new technical simple combination pieces together .Thisis the integration of machinery and the machinery adds the machinery electrificationwhich the electricity forms in the concept basic difference .The mechanicalengineering technology has the merely technical to develop the machineryelectrification still was the traditional machinery its main function still wasreplaces with the enlargement physical strength .But after develops the integrationof machinery micro electron installment besides may substitute for certainmechanical parts the original function but also can entrust with many new functionslike the automatic detection the automatic reduction information demonstrate therecord the automatic control and the control automatic diagnosis and the protectionautomatically and so on .Not only namely the integration of machinery product ishumans hand and body extending humans sense organ and the brains look has theintellectualized characteristic is the integration of machinery and the machineryelectrification distinguishes in the function essence. 2. Integration of machinery development condition integration of machinerydevelopment may divide into 3 stages roughly.20th century 60s before for the firststage this stage is called the initial stage .In this time the people determinationnot on own initiative uses the electronic technology the preliminary achievement toconsummate the mechanical product the performance .Specially in Second World Warperiod the war has stimulated the mechanical product and the electronic technologyunion these mechanical and electrical union military technology postwar transferscivilly to postwar economical restoration positive function .Developed and thedevelopment at that time generally speaking also is at the spontaneouscondition .Because at that time the electronic technology development not yetachieved certain level mechanical technical and electronic technology union alsonot impossible widespread and thorough development already developed the productwas also unable to promote massively. The 20th century 7080 ages for the second stagemay be called the vigorous development stage .This time the computer technologythe control technology the communication development has laid the technology basefor the integration of machinery development . Large-scale ultra large scaleintegrated circuit and microcomputer swift and violent development has provided thefull material base for the integration of machinery development .This timecharacteristic is :①A mechatronics word first generally is accepted in Japanprobably obtains the quite widespread acknowledgment to 1980s last stages in theworldwide scale ②The integration of machinery technology and the product obtainedthe enormous development ③The various countries start to the integration ofmachinery technology and the product give the very big attention and the support.1990s later periods started the integration of machinery technology the new stagewhich makes great strides forward to the intellectualized direction the integrationof machinery enters the thorough development time .At the same time optics thecommunication and so on entered the integration of machinery processes thetechnology also zhan to appear tiny in the integration of machinery the footappeared the light integration of machinery and the micro integration of machineryand so on the new branch On the other hand to the integration ofmachinery systemmodeling design the analysis and the integrated method the integration ofmachinery discipline system and the trend of development has all conducted thethorough research .At the same time because the hugeprogress which domains and so on artificial intelligence technology neural networktechnology and optical fiber technology obtain opened the development vast worldfor the integration of machinery technology .These research will urge theintegration of machinery further to establish the integrity the foundation and formsthe integrity gradually the scientific system. Our country is only then starts fromthe beginning of 1980s in this aspect to study with the application .The State Councilhad been established the integration of machinery leading group and lists as quot863plansquot this technology .When formulated quot95quot the plan and in 2010 developed thesummary had considered fully on international the influence which and possiblybrought from this about the integration of machinery technology developmenttrend .Many universities colleges and institutes the development facility and somelarge and middle scale enterprises have done the massive work to this technicaldevelopment and the application does not yield certain result but and so on theadvanced countries compared with Japan still has the suitable disparity. 3. Integration of machinery trend of development integrations of machinery arethe collection machinery the electron optics the control the computer theinformation and so on the multi-disciplinary overlapping syntheses its developmentand the progress rely on and promote the correlation technology development and theprogress .Therefore the integration of machinery main development direction is asfollows: 3.1 Intellectualized intellectualizations are 21st century integration ofmachinery technological development important development directions .Theartificial intelligence obtains day by day in the integration of machineryconstructors research takes the robot and the numerical control engine bedintellectualization is the important application .Here said quottheintellectualizationquot is to the machine behavior description is in the control theoryfoundation the absorption artificial intelligence the operations research thecomputer science the fuzzy mathematics the psychology the physiology and the chaosdynamics and so on the new thought the new method simulate the human intelligenceenable it to have abilities and so on judgment inference logical thinkingindependent decision-making obtains the higher control goal in order to .Indeedenable the integration of machinery product to have with the human identicalintelligence is not impossible also is nonessential .But the high performancethe high speed microprocessor enable the integration of machinery product to havepreliminary intelligent or humans partial intelligences then is completelypossible and essential. In the modern manufacture process the information has become the controlmanufacture industry the determining factor moreover is the most active actuationfactor .Enhances the manufacture system information-handling capacity to become themodern manufacture science development a key point .As a result of the manufacturesystem information organization and structure multi-level makes the information thegain the integration and the fusion presents draws up the character informationmeasuremulti-dimensional as well as information organizations multi-level .In themanufacture information structural model manufacture information uniform restraintdissemination processing and magnanimous data aspects and so on manufacture knowledgelibrary management all also wait for further break through. Each kind of artificial intelligence tool and the computation intelligence methodpromoted the manufacture intelligence development in the manufacture widespreadapplication .A kind based on the biological evolution algorithm computationintelligent agent in includes thescheduling problem in the combination optimization solution area of technologyreceives the more and more universal attention hopefully completes the combinationoptimization question when the manufacture the solution speed and the solutionprecision aspect breaks through the question scale in pairs the restriction .Themanufacture intelligence also displays in: The intelligent dispatch the intelligentdesign the intelligent processing the robot study the intelligent control theintelligent craft plan the intelligent diagnosis and so on are various These question key breakthrough may form the product innovation the basicresearch system. Between 2 modern mechanical engineering front science differentscience overlapping fusion will have the new science accumulation the economicaldevelopment and societys progress has had the new request and the expectation tothe science and technology thus will form the front science .The front science alsohas solved and between the solution scientific question border area .The front sciencehas the obvious time domain the domain and the dynamic characteristic .The projectfront science distinguished in the general basic science important characteristicis it has covered the key science and technology question which the project actualappeared. Manufacture system is a complex large-scale system for satisfies the manufacturesystem agility the fast response and fast reorganization ability must profit fromthe information science the life sciences and the social sciences and so on themulti-disciplinary research results the exploration manufacture system newarchitecture the manufacture pattern and the manufacture system effectiveoperational mechanism .Makes the system optimization the organizational structureand the good movement condition is makes the system modeling the simulation andthe optimized essential target .Not only the manufacture system new architecture tomakes the enterprise the agility and may reorganize ability to the demand responseability to have the vital significance moreover to made the enterprise first floorproduction equipment the flexibility and may dynamic reorganization ability set ahigher request .The biological manufacture view more and more many is introduced themanufacture system satisfies the manufacture system new request. The study organizes and circulates method and technique of complicated systemfrom the biological phenomenon is a valid exit which will solve many hard nut tocracks that manufacturing industry face from now on currently .Imitating to livingwhat manufacturing point is mimicry living creature organ of from the organizationfrom match more from growth with from evolution etc. function structure and circulatemode of a kind of manufacturing system and manufacturing process. The manufacturing drives in the mechanism under continuously by ones ownperfect raise on organizing structure and circulating modeand thus to adapt theprocess ofwith ability for the environment .For from descend but the last productproceed together a design and make a craft rules the auto of the distance born producesystem of dynamic state reorganization and product and manufacturing the system tendautomatically excellent provided theories foundation and carry out acondition .Imitate to living a manufacturing to belong to manufacturing science andlife science ofquotthe far good luck is miscellaneous to hand overquot it will produceto the manufacturing industry for 21 centuries huge of influence .机电一体化摘要机电一体化是现代科学技术发展的必然结果本文简述了机电一体化技术的基本概要和发展背景。

第47卷第4期Vol.47No.4计算机工程Computer Engineering2021年4月April 2021图神经网络综述王健宗，孔令炜，黄章成，肖京（平安科技（深圳）有限公司联邦学习技术部，广东深圳518063）摘要：随着互联网和计算机信息技术的不断发展，图神经网络已成为人工智能和大数据处理领域的重要研究方向。

图神经网络可对相邻节点间的信息进行有效传播和聚合，并将深度学习理念应用于非欧几里德空间的数据处理中。

简述图计算、图数据库、知识图谱、图神经网络等图结构的相关研究进展，从频域和空间域角度分析与比较基于不同信息聚合方式的图神经网络结构，重点讨论图神经网络与深度学习技术相结合的研究领域，总结归纳图神经网络在动作检测、图系统、文本和图像处理任务中的具体应用，并对图神经网络未来的发展方向进行展望。

关键词：图神经网络；图结构；图计算；深度学习；频域；空间域开放科学（资源服务）标志码（OSID ）：中文引用格式：王健宗，孔令炜，黄章成，等.图神经网络综述［J ］.计算机工程，2021，47（4）：1-12.英文引用格式：WANG Jianzong ，KONG Lingwei ，HUANG Zhangcheng ，et al.Survey of graph neural network ［J ］.Computer Engineering ，2021，47（4）：1-12.Survey of Graph Neural NetworkWANG Jianzong ，KONG Lingwei ，HUANG Zhangcheng ，XIAO Jing（Federated Learning Technology Department ，Ping An Technology （Shenzhen ）Co.，Ltd.，Shenzhen ，Guangdong 518063，China ）【Abstract 】With the continuous development of the computer and Internet technologies ，graph neural network has become an important research area in artificial intelligence and big data.Graph neural network can effectively transmit and aggregate information between neighboring nodes ，and applies the concept of deep learning to the data processing of non-Euclidean space.This paper briefly introduces the research progress of graph computing ，graph database ，knowledge graph ，graph neural network and other graph-based techniques.It also analyses and compares graph neural network structures based on different information aggregation modes in the spectral and spatial domain.Then the paper discusses research fields that combine graph neural network with deep learning ，and summarizes the specific applications of graph neural networks in action detection ，graph systems ，text and image processing tasks.Finally ，it prospects the future development research directions of graph neural networks.【Key words 】graph neural network ；graph structure ；graph computing ；deep learning ；spectral domain ；spatial domain DOI ：10.19678/j.issn.1000-3428.00583820概述近年来，深度学习技术逐渐成为人工智能领域的研究热点和主流发展方向，主要应用于高维特征规则分布的非欧几里德数据处理中，并且在图像处理、语音识别和语义理解［1］等领域取得了显著成果。

机器学习与人工智能领域中常用的英语词汇1.General Concepts (基础概念)•Artificial Intelligence (AI) - 人工智能1)Artificial Intelligence (AI) - 人工智能2)Machine Learning (ML) - 机器学习3)Deep Learning (DL) - 深度学习4)Neural Network - 神经网络5)Natural Language Processing (NLP) - 自然语言处理6)Computer Vision - 计算机视觉7)Robotics - 机器人技术8)Speech Recognition - 语音识别9)Expert Systems - 专家系统10)Knowledge Representation - 知识表示11)Pattern Recognition - 模式识别12)Cognitive Computing - 认知计算13)Autonomous Systems - 自主系统14)Human-Machine Interaction - 人机交互15)Intelligent Agents - 智能代理16)Machine Translation - 机器翻译17)Swarm Intelligence - 群体智能18)Genetic Algorithms - 遗传算法19)Fuzzy Logic - 模糊逻辑20)Reinforcement Learning - 强化学习•Machine Learning (ML) - 机器学习1)Machine Learning (ML) - 机器学习2)Artificial Neural Network - 人工神经网络3)Deep Learning - 深度学习4)Supervised Learning - 有监督学习5)Unsupervised Learning - 无监督学习6)Reinforcement Learning - 强化学习7)Semi-Supervised Learning - 半监督学习8)Training Data - 训练数据9)Test Data - 测试数据10)Validation Data - 验证数据11)Feature - 特征12)Label - 标签13)Model - 模型14)Algorithm - 算法15)Regression - 回归16)Classification - 分类17)Clustering - 聚类18)Dimensionality Reduction - 降维19)Overfitting - 过拟合20)Underfitting - 欠拟合•Deep Learning (DL) - 深度学习1)Deep Learning - 深度学习2)Neural Network - 神经网络3)Artificial Neural Network (ANN) - 人工神经网络4)Convolutional Neural Network (CNN) - 卷积神经网络5)Recurrent Neural Network (RNN) - 循环神经网络6)Long Short-Term Memory (LSTM) - 长短期记忆网络7)Gated Recurrent Unit (GRU) - 门控循环单元8)Autoencoder - 自编码器9)Generative Adversarial Network (GAN) - 生成对抗网络10)Transfer Learning - 迁移学习11)Pre-trained Model - 预训练模型12)Fine-tuning - 微调13)Feature Extraction - 特征提取14)Activation Function - 激活函数15)Loss Function - 损失函数16)Gradient Descent - 梯度下降17)Backpropagation - 反向传播18)Epoch - 训练周期19)Batch Size - 批量大小20)Dropout - 丢弃法•Neural Network - 神经网络1)Neural Network - 神经网络2)Artificial Neural Network (ANN) - 人工神经网络3)Deep Neural Network (DNN) - 深度神经网络4)Convolutional Neural Network (CNN) - 卷积神经网络5)Recurrent Neural Network (RNN) - 循环神经网络6)Long Short-Term Memory (LSTM) - 长短期记忆网络7)Gated Recurrent Unit (GRU) - 门控循环单元8)Feedforward Neural Network - 前馈神经网络9)Multi-layer Perceptron (MLP) - 多层感知器10)Radial Basis Function Network (RBFN) - 径向基函数网络11)Hopfield Network - 霍普菲尔德网络12)Boltzmann Machine - 玻尔兹曼机13)Autoencoder - 自编码器14)Spiking Neural Network (SNN) - 脉冲神经网络15)Self-organizing Map (SOM) - 自组织映射16)Restricted Boltzmann Machine (RBM) - 受限玻尔兹曼机17)Hebbian Learning - 海比安学习18)Competitive Learning - 竞争学习19)Neuroevolutionary - 神经进化20)Neuron - 神经元•Algorithm - 算法1)Algorithm - 算法2)Supervised Learning Algorithm - 有监督学习算法3)Unsupervised Learning Algorithm - 无监督学习算法4)Reinforcement Learning Algorithm - 强化学习算法5)Classification Algorithm - 分类算法6)Regression Algorithm - 回归算法7)Clustering Algorithm - 聚类算法8)Dimensionality Reduction Algorithm - 降维算法9)Decision Tree Algorithm - 决策树算法10)Random Forest Algorithm - 随机森林算法11)Support Vector Machine (SVM) Algorithm - 支持向量机算法12)K-Nearest Neighbors (KNN) Algorithm - K近邻算法13)Naive Bayes Algorithm - 朴素贝叶斯算法14)Gradient Descent Algorithm - 梯度下降算法15)Genetic Algorithm - 遗传算法16)Neural Network Algorithm - 神经网络算法17)Deep Learning Algorithm - 深度学习算法18)Ensemble Learning Algorithm - 集成学习算法19)Reinforcement Learning Algorithm - 强化学习算法20)Metaheuristic Algorithm - 元启发式算法•Model - 模型1)Model - 模型2)Machine Learning Model - 机器学习模型3)Artificial Intelligence Model - 人工智能模型4)Predictive Model - 预测模型5)Classification Model - 分类模型6)Regression Model - 回归模型7)Generative Model - 生成模型8)Discriminative Model - 判别模型9)Probabilistic Model - 概率模型10)Statistical Model - 统计模型11)Neural Network Model - 神经网络模型12)Deep Learning Model - 深度学习模型13)Ensemble Model - 集成模型14)Reinforcement Learning Model - 强化学习模型15)Support Vector Machine (SVM) Model - 支持向量机模型16)Decision Tree Model - 决策树模型17)Random Forest Model - 随机森林模型18)Naive Bayes Model - 朴素贝叶斯模型19)Autoencoder Model - 自编码器模型20)Convolutional Neural Network (CNN) Model - 卷积神经网络模型•Dataset - 数据集1)Dataset - 数据集2)Training Dataset - 训练数据集3)Test Dataset - 测试数据集4)Validation Dataset - 验证数据集5)Balanced Dataset - 平衡数据集6)Imbalanced Dataset - 不平衡数据集7)Synthetic Dataset - 合成数据集8)Benchmark Dataset - 基准数据集9)Open Dataset - 开放数据集10)Labeled Dataset - 标记数据集11)Unlabeled Dataset - 未标记数据集12)Semi-Supervised Dataset - 半监督数据集13)Multiclass Dataset - 多分类数据集14)Feature Set - 特征集15)Data Augmentation - 数据增强16)Data Preprocessing - 数据预处理17)Missing Data - 缺失数据18)Outlier Detection - 异常值检测19)Data Imputation - 数据插补20)Metadata - 元数据•Training - 训练1)Training - 训练2)Training Data - 训练数据3)Training Phase - 训练阶段4)Training Set - 训练集5)Training Examples - 训练样本6)Training Instance - 训练实例7)Training Algorithm - 训练算法8)Training Model - 训练模型9)Training Process - 训练过程10)Training Loss - 训练损失11)Training Epoch - 训练周期12)Training Batch - 训练批次13)Online Training - 在线训练14)Offline Training - 离线训练15)Continuous Training - 连续训练16)Transfer Learning - 迁移学习17)Fine-Tuning - 微调18)Curriculum Learning - 课程学习19)Self-Supervised Learning - 自监督学习20)Active Learning - 主动学习•Testing - 测试1)Testing - 测试2)Test Data - 测试数据3)Test Set - 测试集4)Test Examples - 测试样本5)Test Instance - 测试实例6)Test Phase - 测试阶段7)Test Accuracy - 测试准确率8)Test Loss - 测试损失9)Test Error - 测试错误10)Test Metrics - 测试指标11)Test Suite - 测试套件12)Test Case - 测试用例13)Test Coverage - 测试覆盖率14)Cross-Validation - 交叉验证15)Holdout Validation - 留出验证16)K-Fold Cross-Validation - K折交叉验证17)Stratified Cross-Validation - 分层交叉验证18)Test Driven Development (TDD) - 测试驱动开发19)A/B Testing - A/B 测试20)Model Evaluation - 模型评估•Validation - 验证1)Validation - 验证2)Validation Data - 验证数据3)Validation Set - 验证集4)Validation Examples - 验证样本5)Validation Instance - 验证实例6)Validation Phase - 验证阶段7)Validation Accuracy - 验证准确率8)Validation Loss - 验证损失9)Validation Error - 验证错误10)Validation Metrics - 验证指标11)Cross-Validation - 交叉验证12)Holdout Validation - 留出验证13)K-Fold Cross-Validation - K折交叉验证14)Stratified Cross-Validation - 分层交叉验证15)Leave-One-Out Cross-Validation - 留一法交叉验证16)Validation Curve - 验证曲线17)Hyperparameter Validation - 超参数验证18)Model Validation - 模型验证19)Early Stopping - 提前停止20)Validation Strategy - 验证策略•Supervised Learning - 有监督学习1)Supervised Learning - 有监督学习2)Label - 标签3)Feature - 特征4)Target - 目标5)Training Labels - 训练标签6)Training Features - 训练特征7)Training Targets - 训练目标8)Training Examples - 训练样本9)Training Instance - 训练实例10)Regression - 回归11)Classification - 分类12)Predictor - 预测器13)Regression Model - 回归模型14)Classifier - 分类器15)Decision Tree - 决策树16)Support Vector Machine (SVM) - 支持向量机17)Neural Network - 神经网络18)Feature Engineering - 特征工程19)Model Evaluation - 模型评估20)Overfitting - 过拟合21)Underfitting - 欠拟合22)Bias-Variance Tradeoff - 偏差-方差权衡•Unsupervised Learning - 无监督学习1)Unsupervised Learning - 无监督学习2)Clustering - 聚类3)Dimensionality Reduction - 降维4)Anomaly Detection - 异常检测5)Association Rule Learning - 关联规则学习6)Feature Extraction - 特征提取7)Feature Selection - 特征选择8)K-Means - K均值9)Hierarchical Clustering - 层次聚类10)Density-Based Clustering - 基于密度的聚类11)Principal Component Analysis (PCA) - 主成分分析12)Independent Component Analysis (ICA) - 独立成分分析13)T-distributed Stochastic Neighbor Embedding (t-SNE) - t分布随机邻居嵌入14)Gaussian Mixture Model (GMM) - 高斯混合模型15)Self-Organizing Maps (SOM) - 自组织映射16)Autoencoder - 自动编码器17)Latent Variable - 潜变量18)Data Preprocessing - 数据预处理19)Outlier Detection - 异常值检测20)Clustering Algorithm - 聚类算法•Reinforcement Learning - 强化学习1)Reinforcement Learning - 强化学习2)Agent - 代理3)Environment - 环境4)State - 状态5)Action - 动作6)Reward - 奖励7)Policy - 策略8)Value Function - 值函数9)Q-Learning - Q学习10)Deep Q-Network (DQN) - 深度Q网络11)Policy Gradient - 策略梯度12)Actor-Critic - 演员-评论家13)Exploration - 探索14)Exploitation - 开发15)Temporal Difference (TD) - 时间差分16)Markov Decision Process (MDP) - 马尔可夫决策过程17)State-Action-Reward-State-Action (SARSA) - 状态-动作-奖励-状态-动作18)Policy Iteration - 策略迭代19)Value Iteration - 值迭代20)Monte Carlo Methods - 蒙特卡洛方法•Semi-Supervised Learning - 半监督学习1)Semi-Supervised Learning - 半监督学习2)Labeled Data - 有标签数据3)Unlabeled Data - 无标签数据4)Label Propagation - 标签传播5)Self-Training - 自训练6)Co-Training - 协同训练7)Transudative Learning - 传导学习8)Inductive Learning - 归纳学习9)Manifold Regularization - 流形正则化10)Graph-based Methods - 基于图的方法11)Cluster Assumption - 聚类假设12)Low-Density Separation - 低密度分离13)Semi-Supervised Support Vector Machines (S3VM) - 半监督支持向量机14)Expectation-Maximization (EM) - 期望最大化15)Co-EM - 协同期望最大化16)Entropy-Regularized EM - 熵正则化EM17)Mean Teacher - 平均教师18)Virtual Adversarial Training - 虚拟对抗训练19)Tri-training - 三重训练20)Mix Match - 混合匹配•Feature - 特征1)Feature - 特征2)Feature Engineering - 特征工程3)Feature Extraction - 特征提取4)Feature Selection - 特征选择5)Input Features - 输入特征6)Output Features - 输出特征7)Feature Vector - 特征向量8)Feature Space - 特征空间9)Feature Representation - 特征表示10)Feature Transformation - 特征转换11)Feature Importance - 特征重要性12)Feature Scaling - 特征缩放13)Feature Normalization - 特征归一化14)Feature Encoding - 特征编码15)Feature Fusion - 特征融合16)Feature Dimensionality Reduction - 特征维度减少17)Continuous Feature - 连续特征18)Categorical Feature - 分类特征19)Nominal Feature - 名义特征20)Ordinal Feature - 有序特征•Label - 标签1)Label - 标签2)Labeling - 标注3)Ground Truth - 地面真值4)Class Label - 类别标签5)Target Variable - 目标变量6)Labeling Scheme - 标注方案7)Multi-class Labeling - 多类别标注8)Binary Labeling - 二分类标注9)Label Noise - 标签噪声10)Labeling Error - 标注错误11)Label Propagation - 标签传播12)Unlabeled Data - 无标签数据13)Labeled Data - 有标签数据14)Semi-supervised Learning - 半监督学习15)Active Learning - 主动学习16)Weakly Supervised Learning - 弱监督学习17)Noisy Label Learning - 噪声标签学习18)Self-training - 自训练19)Crowdsourcing Labeling - 众包标注20)Label Smoothing - 标签平滑化•Prediction - 预测1)Prediction - 预测2)Forecasting - 预测3)Regression - 回归4)Classification - 分类5)Time Series Prediction - 时间序列预测6)Forecast Accuracy - 预测准确性7)Predictive Modeling - 预测建模8)Predictive Analytics - 预测分析9)Forecasting Method - 预测方法10)Predictive Performance - 预测性能11)Predictive Power - 预测能力12)Prediction Error - 预测误差13)Prediction Interval - 预测区间14)Prediction Model - 预测模型15)Predictive Uncertainty - 预测不确定性16)Forecast Horizon - 预测时间跨度17)Predictive Maintenance - 预测性维护18)Predictive Policing - 预测式警务19)Predictive Healthcare - 预测性医疗20)Predictive Maintenance - 预测性维护•Classification - 分类1)Classification - 分类2)Classifier - 分类器3)Class - 类别4)Classify - 对数据进行分类5)Class Label - 类别标签6)Binary Classification - 二元分类7)Multiclass Classification - 多类分类8)Class Probability - 类别概率9)Decision Boundary - 决策边界10)Decision Tree - 决策树11)Support Vector Machine (SVM) - 支持向量机12)K-Nearest Neighbors (KNN) - K最近邻算法13)Naive Bayes - 朴素贝叶斯14)Logistic Regression - 逻辑回归15)Random Forest - 随机森林16)Neural Network - 神经网络17)SoftMax Function - SoftMax函数18)One-vs-All (One-vs-Rest) - 一对多(一对剩余)19)Ensemble Learning - 集成学习20)Confusion Matrix - 混淆矩阵•Regression - 回归1)Regression Analysis - 回归分析2)Linear Regression - 线性回归3)Multiple Regression - 多元回归4)Polynomial Regression - 多项式回归5)Logistic Regression - 逻辑回归6)Ridge Regression - 岭回归7)Lasso Regression - Lasso回归8)Elastic Net Regression - 弹性网络回归9)Regression Coefficients - 回归系数10)Residuals - 残差11)Ordinary Least Squares (OLS) - 普通最小二乘法12)Ridge Regression Coefficient - 岭回归系数13)Lasso Regression Coefficient - Lasso回归系数14)Elastic Net Regression Coefficient - 弹性网络回归系数15)Regression Line - 回归线16)Prediction Error - 预测误差17)Regression Model - 回归模型18)Nonlinear Regression - 非线性回归19)Generalized Linear Models (GLM) - 广义线性模型20)Coefficient of Determination (R-squared) - 决定系数21)F-test - F检验22)Homoscedasticity - 同方差性23)Heteroscedasticity - 异方差性24)Autocorrelation - 自相关25)Multicollinearity - 多重共线性26)Outliers - 异常值27)Cross-validation - 交叉验证28)Feature Selection - 特征选择29)Feature Engineering - 特征工程30)Regularization - 正则化2.Neural Networks and Deep Learning (神经网络与深度学习)•Convolutional Neural Network (CNN) - 卷积神经网络1)Convolutional Neural Network (CNN) - 卷积神经网络2)Convolution Layer - 卷积层3)Feature Map - 特征图4)Convolution Operation - 卷积操作5)Stride - 步幅6)Padding - 填充7)Pooling Layer - 池化层8)Max Pooling - 最大池化9)Average Pooling - 平均池化10)Fully Connected Layer - 全连接层11)Activation Function - 激活函数12)Rectified Linear Unit (ReLU) - 线性修正单元13)Dropout - 随机失活14)Batch Normalization - 批量归一化15)Transfer Learning - 迁移学习16)Fine-Tuning - 微调17)Image Classification - 图像分类18)Object Detection - 物体检测19)Semantic Segmentation - 语义分割20)Instance Segmentation - 实例分割21)Generative Adversarial Network (GAN) - 生成对抗网络22)Image Generation - 图像生成23)Style Transfer - 风格迁移24)Convolutional Autoencoder - 卷积自编码器25)Recurrent Neural Network (RNN) - 循环神经网络•Recurrent Neural Network (RNN) - 循环神经网络1)Recurrent Neural Network (RNN) - 循环神经网络2)Long Short-Term Memory (LSTM) - 长短期记忆网络3)Gated Recurrent Unit (GRU) - 门控循环单元4)Sequence Modeling - 序列建模5)Time Series Prediction - 时间序列预测6)Natural Language Processing (NLP) - 自然语言处理7)Text Generation - 文本生成8)Sentiment Analysis - 情感分析9)Named Entity Recognition (NER) - 命名实体识别10)Part-of-Speech Tagging (POS Tagging) - 词性标注11)Sequence-to-Sequence (Seq2Seq) - 序列到序列12)Attention Mechanism - 注意力机制13)Encoder-Decoder Architecture - 编码器-解码器架构14)Bidirectional RNN - 双向循环神经网络15)Teacher Forcing - 强制教师法16)Backpropagation Through Time (BPTT) - 通过时间的反向传播17)Vanishing Gradient Problem - 梯度消失问题18)Exploding Gradient Problem - 梯度爆炸问题19)Language Modeling - 语言建模20)Speech Recognition - 语音识别•Long Short-Term Memory (LSTM) - 长短期记忆网络1)Long Short-Term Memory (LSTM) - 长短期记忆网络2)Cell State - 细胞状态3)Hidden State - 隐藏状态4)Forget Gate - 遗忘门5)Input Gate - 输入门6)Output Gate - 输出门7)Peephole Connections - 窥视孔连接8)Gated Recurrent Unit (GRU) - 门控循环单元9)Vanishing Gradient Problem - 梯度消失问题10)Exploding Gradient Problem - 梯度爆炸问题11)Sequence Modeling - 序列建模12)Time Series Prediction - 时间序列预测13)Natural Language Processing (NLP) - 自然语言处理14)Text Generation - 文本生成15)Sentiment Analysis - 情感分析16)Named Entity Recognition (NER) - 命名实体识别17)Part-of-Speech Tagging (POS Tagging) - 词性标注18)Attention Mechanism - 注意力机制19)Encoder-Decoder Architecture - 编码器-解码器架构20)Bidirectional LSTM - 双向长短期记忆网络•Attention Mechanism - 注意力机制1)Attention Mechanism - 注意力机制2)Self-Attention - 自注意力3)Multi-Head Attention - 多头注意力4)Transformer - 变换器5)Query - 查询6)Key - 键7)Value - 值8)Query-Value Attention - 查询-值注意力9)Dot-Product Attention - 点积注意力10)Scaled Dot-Product Attention - 缩放点积注意力11)Additive Attention - 加性注意力12)Context Vector - 上下文向量13)Attention Score - 注意力分数14)SoftMax Function - SoftMax函数15)Attention Weight - 注意力权重16)Global Attention - 全局注意力17)Local Attention - 局部注意力18)Positional Encoding - 位置编码19)Encoder-Decoder Attention - 编码器-解码器注意力20)Cross-Modal Attention - 跨模态注意力•Generative Adversarial Network (GAN) - 生成对抗网络1)Generative Adversarial Network (GAN) - 生成对抗网络2)Generator - 生成器3)Discriminator - 判别器4)Adversarial Training - 对抗训练5)Minimax Game - 极小极大博弈6)Nash Equilibrium - 纳什均衡7)Mode Collapse - 模式崩溃8)Training Stability - 训练稳定性9)Loss Function - 损失函数10)Discriminative Loss - 判别损失11)Generative Loss - 生成损失12)Wasserstein GAN (WGAN) - Wasserstein GAN（WGAN）13)Deep Convolutional GAN (DCGAN) - 深度卷积生成对抗网络（DCGAN）14)Conditional GAN (c GAN) - 条件生成对抗网络（c GAN）15)Style GAN - 风格生成对抗网络16)Cycle GAN - 循环生成对抗网络17)Progressive Growing GAN (PGGAN) - 渐进式增长生成对抗网络（PGGAN）18)Self-Attention GAN (SAGAN) - 自注意力生成对抗网络（SAGAN）19)Big GAN - 大规模生成对抗网络20)Adversarial Examples - 对抗样本•Encoder-Decoder - 编码器-解码器1)Encoder-Decoder Architecture - 编码器-解码器架构2)Encoder - 编码器3)Decoder - 解码器4)Sequence-to-Sequence Model (Seq2Seq) - 序列到序列模型5)State Vector - 状态向量6)Context Vector - 上下文向量7)Hidden State - 隐藏状态8)Attention Mechanism - 注意力机制9)Teacher Forcing - 强制教师法10)Beam Search - 束搜索11)Recurrent Neural Network (RNN) - 循环神经网络12)Long Short-Term Memory (LSTM) - 长短期记忆网络13)Gated Recurrent Unit (GRU) - 门控循环单元14)Bidirectional Encoder - 双向编码器15)Greedy Decoding - 贪婪解码16)Masking - 遮盖17)Dropout - 随机失活18)Embedding Layer - 嵌入层19)Cross-Entropy Loss - 交叉熵损失20)Tokenization - 令牌化•Transfer Learning - 迁移学习1)Transfer Learning - 迁移学习2)Source Domain - 源领域3)Target Domain - 目标领域4)Fine-Tuning - 微调5)Domain Adaptation - 领域自适应6)Pre-Trained Model - 预训练模型7)Feature Extraction - 特征提取8)Knowledge Transfer - 知识迁移9)Unsupervised Domain Adaptation - 无监督领域自适应10)Semi-Supervised Domain Adaptation - 半监督领域自适应11)Multi-Task Learning - 多任务学习12)Data Augmentation - 数据增强13)Task Transfer - 任务迁移14)Model Agnostic Meta-Learning (MAML) - 与模型无关的元学习（MAML）15)One-Shot Learning - 单样本学习16)Zero-Shot Learning - 零样本学习17)Few-Shot Learning - 少样本学习18)Knowledge Distillation - 知识蒸馏19)Representation Learning - 表征学习20)Adversarial Transfer Learning - 对抗迁移学习•Pre-trained Models - 预训练模型1)Pre-trained Model - 预训练模型2)Transfer Learning - 迁移学习3)Fine-Tuning - 微调4)Knowledge Transfer - 知识迁移5)Domain Adaptation - 领域自适应6)Feature Extraction - 特征提取7)Representation Learning - 表征学习8)Language Model - 语言模型9)Bidirectional Encoder Representations from Transformers (BERT) - 双向编码器结构转换器10)Generative Pre-trained Transformer (GPT) - 生成式预训练转换器11)Transformer-based Models - 基于转换器的模型12)Masked Language Model (MLM) - 掩蔽语言模型13)Cloze Task - 填空任务14)Tokenization - 令牌化15)Word Embeddings - 词嵌入16)Sentence Embeddings - 句子嵌入17)Contextual Embeddings - 上下文嵌入18)Self-Supervised Learning - 自监督学习19)Large-Scale Pre-trained Models - 大规模预训练模型•Loss Function - 损失函数1)Loss Function - 损失函数2)Mean Squared Error (MSE) - 均方误差3)Mean Absolute Error (MAE) - 平均绝对误差4)Cross-Entropy Loss - 交叉熵损失5)Binary Cross-Entropy Loss - 二元交叉熵损失6)Categorical Cross-Entropy Loss - 分类交叉熵损失7)Hinge Loss - 合页损失8)Huber Loss - Huber损失9)Wasserstein Distance - Wasserstein距离10)Triplet Loss - 三元组损失11)Contrastive Loss - 对比损失12)Dice Loss - Dice损失13)Focal Loss - 焦点损失14)GAN Loss - GAN损失15)Adversarial Loss - 对抗损失16)L1 Loss - L1损失17)L2 Loss - L2损失18)Huber Loss - Huber损失19)Quantile Loss - 分位数损失•Activation Function - 激活函数1)Activation Function - 激活函数2)Sigmoid Function - Sigmoid函数3)Hyperbolic Tangent Function (Tanh) - 双曲正切函数4)Rectified Linear Unit (Re LU) - 矩形线性单元5)Parametric Re LU (P Re LU) - 参数化Re LU6)Exponential Linear Unit (ELU) - 指数线性单元7)Swish Function - Swish函数8)Softplus Function - Soft plus函数9)Softmax Function - SoftMax函数10)Hard Tanh Function - 硬双曲正切函数11)Softsign Function - Softsign函数12)GELU (Gaussian Error Linear Unit) - GELU（高斯误差线性单元）13)Mish Function - Mish函数14)CELU (Continuous Exponential Linear Unit) - CELU（连续指数线性单元）15)Bent Identity Function - 弯曲恒等函数16)Gaussian Error Linear Units (GELUs) - 高斯误差线性单元17)Adaptive Piecewise Linear (APL) - 自适应分段线性函数18)Radial Basis Function (RBF) - 径向基函数•Backpropagation - 反向传播1)Backpropagation - 反向传播2)Gradient Descent - 梯度下降3)Partial Derivative - 偏导数4)Chain Rule - 链式法则5)Forward Pass - 前向传播6)Backward Pass - 反向传播7)Computational Graph - 计算图8)Neural Network - 神经网络9)Loss Function - 损失函数10)Gradient Calculation - 梯度计算11)Weight Update - 权重更新12)Activation Function - 激活函数13)Optimizer - 优化器14)Learning Rate - 学习率15)Mini-Batch Gradient Descent - 小批量梯度下降16)Stochastic Gradient Descent (SGD) - 随机梯度下降17)Batch Gradient Descent - 批量梯度下降18)Momentum - 动量19)Adam Optimizer - Adam优化器20)Learning Rate Decay - 学习率衰减•Gradient Descent - 梯度下降1)Gradient Descent - 梯度下降2)Stochastic Gradient Descent (SGD) - 随机梯度下降3)Mini-Batch Gradient Descent - 小批量梯度下降4)Batch Gradient Descent - 批量梯度下降5)Learning Rate - 学习率6)Momentum - 动量7)Adaptive Moment Estimation (Adam) - 自适应矩估计8)RMSprop - 均方根传播9)Learning Rate Schedule - 学习率调度10)Convergence - 收敛11)Divergence - 发散12)Adagrad - 自适应学习速率方法13)Adadelta - 自适应增量学习率方法14)Adamax - 自适应矩估计的扩展版本15)Nadam - Nesterov Accelerated Adaptive Moment Estimation16)Learning Rate Decay - 学习率衰减17)Step Size - 步长18)Conjugate Gradient Descent - 共轭梯度下降19)Line Search - 线搜索20)Newton's Method - 牛顿法•Learning Rate - 学习率1)Learning Rate - 学习率2)Adaptive Learning Rate - 自适应学习率3)Learning Rate Decay - 学习率衰减4)Initial Learning Rate - 初始学习率5)Step Size - 步长6)Momentum - 动量7)Exponential Decay - 指数衰减8)Annealing - 退火9)Cyclical Learning Rate - 循环学习率10)Learning Rate Schedule - 学习率调度11)Warm-up - 预热12)Learning Rate Policy - 学习率策略13)Learning Rate Annealing - 学习率退火14)Cosine Annealing - 余弦退火15)Gradient Clipping - 梯度裁剪16)Adapting Learning Rate - 适应学习率17)Learning Rate Multiplier - 学习率倍增器18)Learning Rate Reduction - 学习率降低19)Learning Rate Update - 学习率更新20)Scheduled Learning Rate - 定期学习率•Batch Size - 批量大小1)Batch Size - 批量大小2)Mini-Batch - 小批量3)Batch Gradient Descent - 批量梯度下降4)Stochastic Gradient Descent (SGD) - 随机梯度下降5)Mini-Batch Gradient Descent - 小批量梯度下降6)Online Learning - 在线学习7)Full-Batch - 全批量8)Data Batch - 数据批次9)Training Batch - 训练批次10)Batch Normalization - 批量归一化11)Batch-wise Optimization - 批量优化12)Batch Processing - 批量处理13)Batch Sampling - 批量采样14)Adaptive Batch Size - 自适应批量大小15)Batch Splitting - 批量分割16)Dynamic Batch Size - 动态批量大小17)Fixed Batch Size - 固定批量大小18)Batch-wise Inference - 批量推理19)Batch-wise Training - 批量训练20)Batch Shuffling - 批量洗牌•Epoch - 训练周期1)Training Epoch - 训练周期2)Epoch Size - 周期大小3)Early Stopping - 提前停止4)Validation Set - 验证集5)Training Set - 训练集6)Test Set - 测试集7)Overfitting - 过拟合8)Underfitting - 欠拟合9)Model Evaluation - 模型评估10)Model Selection - 模型选择11)Hyperparameter Tuning - 超参数调优12)Cross-Validation - 交叉验证13)K-fold Cross-Validation - K折交叉验证14)Stratified Cross-Validation - 分层交叉验证15)Leave-One-Out Cross-Validation (LOOCV) - 留一法交叉验证16)Grid Search - 网格搜索17)Random Search - 随机搜索18)Model Complexity - 模型复杂度19)Learning Curve - 学习曲线20)Convergence - 收敛3.Machine Learning Techniques and Algorithms (机器学习技术与算法)•Decision Tree - 决策树1)Decision Tree - 决策树2)Node - 节点3)Root Node - 根节点4)Leaf Node - 叶节点5)Internal Node - 内部节点6)Splitting Criterion - 分裂准则7)Gini Impurity - 基尼不纯度8)Entropy - 熵9)Information Gain - 信息增益10)Gain Ratio - 增益率11)Pruning - 剪枝12)Recursive Partitioning - 递归分割13)CART (Classification and Regression Trees) - 分类回归树14)ID3 (Iterative Dichotomiser 3) - 迭代二叉树315)C4.5 (successor of ID3) - C4.5（ID3的后继者）16)C5.0 (successor of C4.5) - C5.0（C4.5的后继者）17)Split Point - 分裂点18)Decision Boundary - 决策边界19)Pruned Tree - 剪枝后的树20)Decision Tree Ensemble - 决策树集成•Random Forest - 随机森林1)Random Forest - 随机森林2)Ensemble Learning - 集成学习3)Bootstrap Sampling - 自助采样4)Bagging (Bootstrap Aggregating) - 装袋法5)Out-of-Bag (OOB) Error - 袋外误差6)Feature Subset - 特征子集7)Decision Tree - 决策树8)Base Estimator - 基础估计器9)Tree Depth - 树深度10)Randomization - 随机化11)Majority Voting - 多数投票12)Feature Importance - 特征重要性13)OOB Score - 袋外得分14)Forest Size - 森林大小15)Max Features - 最大特征数16)Min Samples Split - 最小分裂样本数17)Min Samples Leaf - 最小叶节点样本数18)Gini Impurity - 基尼不纯度19)Entropy - 熵20)Variable Importance - 变量重要性•Support Vector Machine (SVM) - 支持向量机1)Support Vector Machine (SVM) - 支持向量机2)Hyperplane - 超平面3)Kernel Trick - 核技巧4)Kernel Function - 核函数5)Margin - 间隔6)Support Vectors - 支持向量7)Decision Boundary - 决策边界8)Maximum Margin Classifier - 最大间隔分类器9)Soft Margin Classifier - 软间隔分类器10) C Parameter - C参数11)Radial Basis Function (RBF) Kernel - 径向基函数核12)Polynomial Kernel - 多项式核13)Linear Kernel - 线性核14)Quadratic Kernel - 二次核15)Gaussian Kernel - 高斯核16)Regularization - 正则化17)Dual Problem - 对偶问题18)Primal Problem - 原始问题19)Kernelized SVM - 核化支持向量机20)Multiclass SVM - 多类支持向量机•K-Nearest Neighbors (KNN) - K-最近邻1)K-Nearest Neighbors (KNN) - K-最近邻2)Nearest Neighbor - 最近邻3)Distance Metric - 距离度量4)Euclidean Distance - 欧氏距离5)Manhattan Distance - 曼哈顿距离6)Minkowski Distance - 闵可夫斯基距离7)Cosine Similarity - 余弦相似度8)K Value - K值9)Majority Voting - 多数投票10)Weighted KNN - 加权KNN11)Radius Neighbors - 半径邻居12)Ball Tree - 球树13)KD Tree - KD树14)Locality-Sensitive Hashing (LSH) - 局部敏感哈希15)Curse of Dimensionality - 维度灾难16)Class Label - 类标签17)Training Set - 训练集18)Test Set - 测试集19)Validation Set - 验证集20)Cross-Validation - 交叉验证•Naive Bayes - 朴素贝叶斯1)Naive Bayes - 朴素贝叶斯2)Bayes' Theorem - 贝叶斯定理3)Prior Probability - 先验概率4)Posterior Probability - 后验概率5)Likelihood - 似然6)Class Conditional Probability - 类条件概率7)Feature Independence Assumption - 特征独立假设8)Multinomial Naive Bayes - 多项式朴素贝叶斯9)Gaussian Naive Bayes - 高斯朴素贝叶斯10)Bernoulli Naive Bayes - 伯努利朴素贝叶斯11)Laplace Smoothing - 拉普拉斯平滑12)Add-One Smoothing - 加一平滑13)Maximum A Posteriori (MAP) - 最大后验概率14)Maximum Likelihood Estimation (MLE) - 最大似然估计15)Classification - 分类16)Feature Vectors - 特征向量17)Training Set - 训练集18)Test Set - 测试集19)Class Label - 类标签20)Confusion Matrix - 混淆矩阵•Clustering - 聚类1)Clustering - 聚类2)Centroid - 质心3)Cluster Analysis - 聚类分析4)Partitioning Clustering - 划分式聚类5)Hierarchical Clustering - 层次聚类6)Density-Based Clustering - 基于密度的聚类7)K-Means Clustering - K均值聚类8)K-Medoids Clustering - K中心点聚类9)DBSCAN (Density-Based Spatial Clustering of Applications with Noise) - 基于密度的空间聚类算法10)Agglomerative Clustering - 聚合式聚类11)Dendrogram - 系统树图12)Silhouette Score - 轮廓系数13)Elbow Method - 肘部法则14)Clustering Validation - 聚类验证15)Intra-cluster Distance - 类内距离16)Inter-cluster Distance - 类间距离17)Cluster Cohesion - 类内连贯性18)Cluster Separation - 类间分离度19)Cluster Assignment - 聚类分配20)Cluster Label - 聚类标签•K-Means - K-均值1)K-Means - K-均值2)Centroid - 质心3)Cluster - 聚类4)Cluster Center - 聚类中心5)Cluster Assignment - 聚类分配6)Cluster Analysis - 聚类分析7)K Value - K值8)Elbow Method - 肘部法则9)Inertia - 惯性10)Silhouette Score - 轮廓系数11)Convergence - 收敛12)Initialization - 初始化13)Euclidean Distance - 欧氏距离14)Manhattan Distance - 曼哈顿距离15)Distance Metric - 距离度量16)Cluster Radius - 聚类半径17)Within-Cluster Variation - 类内变异18)Cluster Quality - 聚类质量19)Clustering Algorithm - 聚类算法20)Clustering Validation - 聚类验证•Dimensionality Reduction - 降维1)Dimensionality Reduction - 降维2)Feature Extraction - 特征提取3)Feature Selection - 特征选择4)Principal Component Analysis (PCA) - 主成分分析5)Singular Value Decomposition (SVD) - 奇异值分解6)Linear Discriminant Analysis (LDA) - 线性判别分析7)t-Distributed Stochastic Neighbor Embedding (t-SNE) - t-分布随机邻域嵌入8)Autoencoder - 自编码器9)Manifold Learning - 流形学习10)Locally Linear Embedding (LLE) - 局部线性嵌入11)Isomap - 等度量映射12)Uniform Manifold Approximation and Projection (UMAP) - 均匀流形逼近与投影13)Kernel PCA - 核主成分分析14)Non-negative Matrix Factorization (NMF) - 非负矩阵分解15)Independent Component Analysis (ICA) - 独立成分分析16)Variational Autoencoder (VAE) - 变分自编码器17)Sparse Coding - 稀疏编码18)Random Projection - 随机投影19)Neighborhood Preserving Embedding (NPE) - 保持邻域结构的嵌入20)Curvilinear Component Analysis (CCA) - 曲线成分分析•Principal Component Analysis (PCA) - 主成分分析1)Principal Component Analysis (PCA) - 主成分分析2)Eigenvector - 特征向量3)Eigenvalue - 特征值4)Covariance Matrix - 协方差矩阵。

人工智能神经网络人工智能神经网络（Artificial Neural Networks，ANN）是一种模拟人脑神经网络的计算模型。

它由一些简单的单元（神经元）组成，每个神经元都接收一些输入，并生成相关的输出。

神经元之间通过一些连接（权重）相互作用，以完成某些任务。

神经元神经元是神经网络中的基本单元，每个神经元都有多个输入和一个输出。

输入传递到神经元中，通过一些计算生成输出。

在人工神经网络中，神经元的模型是将所有输入加权求和，将权重乘以输入值并加上偏差值（bias），然后将结果带入激活函数中。

激活函数决定神经元的输出。

不同类型的神经元使用不同的激活函数，如Sigmond函数、ReLU函数等。

每个神经元的输出可以是其他神经元的输入，这些连接和权重形成了一个图，即神经网络。

神经网络神经网络是一种由多个神经元组成的计算模型。

它以输入作为网络的初始状态，将信息传递到网络的每个神经元中，并通过训练来调整连接和权重值，以产生期望的输出。

神经网络的目的是通过学习输入和输出之间的关系来预测新数据的输出。

神经网络的设计采用层次结构，它由不同数量、形式和顺序的神经元组成。

最简单的网络模型是单层感知器模型，它只有一个神经元层。

多层神经网络模型包括两种基本结构：前向传播神经网络和循环神经网络。

前向传播神经网络也称为一次性神经网络，通过将输入传递到一个或多个隐藏层，并生成输出。

循环神经网络采用时间序列的概念，它的输出不仅与当前的输入有关，还与以前的输入有关。

训练训练神经网络是调整其连接和权重值以达到期望输出的过程。

训练的目的是最小化训练误差，也称为损失函数。

训练误差是神经网络输出与期望输出之间的差异。

通过训练，可以将网络中的权重和偏置调整到最佳值，以最大程度地减小训练误差。

神经网络的训练过程通常有两种主要方法：1.前向传播: 在此方法中，神经网络的输入通过网络经过一种学习算法来逐步计算，调整每个神经元的权重和偏置，以尽可能地减小误差。

探索人工智能的前沿学术期刊随着科技的不断迭代和创新，人工智能（Artificial Intelligence, AI）的研究和应用领域也在不断扩大。

想要了解和掌握人工智能领域最新进展和前沿研究，学术期刊是不可或缺的资源。

本文将介绍几种国际知名的人工智能学术期刊，并简要分析它们的特点和优势。

一、IEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Transactions on Pattern Analysis and Machine Intelligence （TPAMI）是一本涵盖模式分析与机器智能领域的顶尖学术期刊，由IEEE计算机学会出版。

该期刊聚焦于计算机视觉、模式识别和机器学习等学科领域。

TPAMI刊登的论文经过严格的评审和筛选，具备高度的学术性和技术规范。

它的论文质量严格、内容深入，对于研究人员和学术界从事人工智能相关领域的研究提供了重要的参考和借鉴。

二、Journal of Artificial Intelligence ResearchJournal of Artificial Intelligence Research（JAIR）是专门发表人工智能相关研究成果的顶尖学术期刊。

它以高水平的学术论文和研究报告为主要核心内容，内容广泛涵盖机器学习、知识表示、智能系统等领域。

JAIR以其学术性和创新性而受到广泛关注和认可。

它的论文在学术界影响力大，对人工智能领域的研究发展具有重要的推动作用。

三、Artificial Intelligence JournalArtificial Intelligence Journal（AIJ）是世界上最重要的人工智能学术期刊之一，发表有关人工智能领域的高质量论文。

AIJ鼓励创新性和原创性的研究，特别注重交叉学科的融合。

该期刊的论文风格多样，涵盖的范围广泛，从理论基础到实践应用都有涉及。

摘要现代化的建设需要信息技术的支持，专家系统是一种智能化的信息技术，它的应用改变了过去社会各领域生产基层领导者决策的盲目性和主观性，缓解了我国各领域技术推广人员不足的矛盾，促进了社会的持续发展。

但传统专家系统只能处理显性的表面的知识，存在推理能力弱，智能水平低等缺点，所以本文引入了神经网络技术来克服传统专家系统的不足，来试图解决专家系统中存在的关系复杂、边界模糊等难于用规则或数学模型严格描述的问题。

本文采用神经网络进行大部分的知识获取及推理功能，将网络输出结果转换成专家系统推理机能接受的形式，由专家系统的推理机得到问题的最后结果。

最后，根据论文中的理论建造了棉铃虫害预测的专家系统，能够准确预测棉铃虫的发病程度，并能给用户提出防治建议及措施。

有力地说明了本论文中所建造的专家系统在一定程度上解决了传统专家系统在知识获取上的“瓶颈”问题，实现了神经网络的并行推理，神经网络在专家系统中的应用具有较好的发展前景。

关键词神经网络专家系统推理机面向对象知识获取AbstractModern construction needs the support of IT, expert system is the IT of a kind of intelligence, its application has changed past social each field production subjectivity and the blindness of grass-roots leader decision-making, have alleviated the contradiction that each field technical popularization of our country has insufficient people, the continued development that has promoted society. But traditional expert system can only handle the surface of dominance knowledge, existence has weak inference ability, intelligent level is low, so this paper has led into artificial neural network technology to surmount the deficiency of traditional expert system, attempt the relation that solution has in expert system complex, boundary is fuzzy etc. are hard to describe strictly with regular or mathematics model. This paper carries out the most of knowledge with neural network to get and infer function , changes network output as a result into expert system, inference function the form of accepting , the inference machine from expert system gets the final result of problem. Finally, have built the expert system of the cotton bell forecast of insect pest according to the theory in this thesis, can accurate forecast cotton bell insect become sick degree, and can make prevention suggestion and measure to user. Have proved on certain degree the expert system built using this tool have solved traditional expert system in knowledge the problem of " bottleneck " that gotten , the parallel inference that has realized neural network, Neural network in expert system application has the better prospect for development.Key words Neural network Expert system Reasoning engineObject-orientation Knowledge acquisition目录摘要 (I)Abstract (II)第1章绪论 (1)1.1 论文研究的背景 (1)1.1.1 国内外研究现状 (1)1.1.2 专家系统在开发使用中存在的缺点 (2)1.1.3 神经网络的局限性 (3)1.2 论文研究的主要内容 (3)1.3 论文研究的目标及意义 (4)1.4 论文的组织结构和安排 (4)第2章神经网络和专家系统的基本理论 (5)2.1 神经网络的基本理论 (5)2.1.1 神经网络的概述及工作原理 (5)2.1.2 神经网络的基本特征及优点 (6)2.1.3 BP神经网络模型 (8)2.1.4 BP网络结构设置 (10)2.2 专家系统的基本理论 (12)2.2.1 专家系统的功能 (12)2.2.2 专家系统的基本结构及组成 (13)第3章基于神经网络专家系统的研究 (16)3.1 神经网络专家系统整体设计 (16)3.1.1 神经网络专家系统总体结构 (16)3.1.2 神经网络专家系统的组成及功能 (16)3.2 知识表示 (17)3.2.1 传统知识表示方法 (18)3.2.2 面向对象知识表示方法 (19)3.2.3 本论文采用的知识表示方法 (20)3.3 知识获取 (21)3.3.1 知识获取的基本方法 (22)3.3.2 神经网络知识获取方法 (23)3.4 推理机 (25)3.4.1 专家系统推理机制 (25)3.4.2 神经网络专家系统的推理机制 (26)3.5 知识存储与维护更新 (26)3.5.1 神经网络知识存储 (26)3.5.2 神经网络知识维护更新 (27)3.6 用户界面 (27)第4章基于神经网络专家系统的应用 (29)4.1 例子的建造背景 (29)4.2 例子的建造过程 (30)4.2.1 特征因子选择 (30)4.2.2 网络参数配置 (30)4.2.3 样本数据处理 (31)4.2.4 训练网络 (31)4.2.5 网络训练结果分析 (34)4.2.6 专家建议 (34)4.3 例子的结果分析 (34)结论 (36)致谢 (37)参考文献 (38)附录1 外文资料中文翻译 (40)附录2 外文资料原文 (45)第1章绪论1.1 论文研究的背景专家系统(Expert System，缩写ES)是人工智能领域应用研究最活跃的领域之一，日益得到广泛的应用。