Connectivity reflects coding a model of voltage-based STDP with homeostasis

名词解释中英文对比<using_information_sources> social networks 社会网络abductive reasoning 溯因推理action recognition(行为识别)active learning(主动学习)adaptive systems 自适应系统adverse drugs reactions(药物不良反应)algorithm design and analysis(算法设计与分析) algorithm(算法)artificial intelligence 人工智能association rule(关联规则)attribute value taxonomy 属性分类规范automomous agent 自动代理automomous systems 自动系统background knowledge 背景知识bayes methods(贝叶斯方法)bayesian inference(贝叶斯推断)bayesian methods(bayes 方法)belief propagation(置信传播)better understanding 内涵理解big data 大数据big data(大数据)biological network(生物网络)biological sciences(生物科学)biomedical domain 生物医学领域biomedical research(生物医学研究)biomedical text(生物医学文本)boltzmann machine(玻尔兹曼机)bootstrapping method 拔靴法case based reasoning 实例推理causual models 因果模型citation matching (引文匹配)classification (分类)classification algorithms(分类算法)clistering algorithms 聚类算法cloud computing(云计算)cluster-based retrieval (聚类检索)clustering (聚类)clustering algorithms(聚类算法)clustering 聚类cognitive science 认知科学collaborative filtering (协同过滤)collaborative filtering(协同过滤)collabrative ontology development 联合本体开发collabrative ontology engineering 联合本体工程commonsense knowledge 常识communication networks(通讯网络)community detection(社区发现)complex data(复杂数据)complex dynamical networks(复杂动态网络)complex network(复杂网络)complex network(复杂网络)computational biology 计算生物学computational biology(计算生物学)computational complexity(计算复杂性) computational intelligence 智能计算computational modeling(计算模型)computer animation(计算机动画)computer networks(计算机网络)computer science 计算机科学concept clustering 概念聚类concept formation 概念形成concept learning 概念学习concept map 概念图concept model 概念模型concept modelling 概念模型conceptual model 概念模型conditional random field(条件随机场模型) conjunctive quries 合取查询constrained least squares (约束最小二乘) convex programming(凸规划)convolutional neural networks(卷积神经网络) customer relationship management(客户关系管理) data analysis(数据分析)data analysis(数据分析)data center(数据中心)data clustering (数据聚类)data compression(数据压缩)data envelopment analysis (数据包络分析)data fusion 数据融合data generation(数据生成)data handling(数据处理)data hierarchy (数据层次)data integration(数据整合)data integrity 数据完整性data intensive computing(数据密集型计算)data management 数据管理data management(数据管理)data management(数据管理)data miningdata mining 数据挖掘data model 数据模型data models(数据模型)data partitioning 数据划分data point(数据点)data privacy(数据隐私)data security(数据安全)data stream(数据流)data streams(数据流)data structure( 数据结构)data structure(数据结构)data visualisation(数据可视化)data visualization 数据可视化data visualization(数据可视化)data warehouse(数据仓库)data warehouses(数据仓库)data warehousing(数据仓库)database management systems(数据库管理系统)database management(数据库管理)date interlinking 日期互联date linking 日期链接Decision analysis(决策分析)decision maker 决策者decision making (决策)decision models 决策模型decision models 决策模型decision rule 决策规则decision support system 决策支持系统decision support systems (决策支持系统) decision tree(决策树)decission tree 决策树deep belief network(深度信念网络)deep learning(深度学习)defult reasoning 默认推理density estimation(密度估计)design methodology 设计方法论dimension reduction(降维) dimensionality reduction(降维)directed graph(有向图)disaster management 灾害管理disastrous event(灾难性事件)discovery(知识发现)dissimilarity (相异性)distributed databases 分布式数据库distributed databases(分布式数据库) distributed query 分布式查询document clustering (文档聚类)domain experts 领域专家domain knowledge 领域知识domain specific language 领域专用语言dynamic databases(动态数据库)dynamic logic 动态逻辑dynamic network(动态网络)dynamic system(动态系统)earth mover's distance(EMD 距离) education 教育efficient algorithm(有效算法)electric commerce 电子商务electronic health records(电子健康档案) entity disambiguation 实体消歧entity recognition 实体识别entity recognition(实体识别)entity resolution 实体解析event detection 事件检测event detection(事件检测)event extraction 事件抽取event identificaton 事件识别exhaustive indexing 完整索引expert system 专家系统expert systems(专家系统)explanation based learning 解释学习factor graph(因子图)feature extraction 特征提取feature extraction(特征提取)feature extraction(特征提取)feature selection (特征选择)feature selection 特征选择feature selection(特征选择)feature space 特征空间first order logic 一阶逻辑formal logic 形式逻辑formal meaning prepresentation 形式意义表示formal semantics 形式语义formal specification 形式描述frame based system 框为本的系统frequent itemsets(频繁项目集)frequent pattern(频繁模式)fuzzy clustering (模糊聚类)fuzzy clustering (模糊聚类)fuzzy clustering (模糊聚类)fuzzy data mining(模糊数据挖掘)fuzzy logic 模糊逻辑fuzzy set theory(模糊集合论)fuzzy set(模糊集)fuzzy sets 模糊集合fuzzy systems 模糊系统gaussian processes(高斯过程)gene expression data 基因表达数据gene expression(基因表达)generative model(生成模型)generative model(生成模型)genetic algorithm 遗传算法genome wide association study(全基因组关联分析) graph classification(图分类)graph classification(图分类)graph clustering(图聚类)graph data(图数据)graph data(图形数据)graph database 图数据库graph database(图数据库)graph mining(图挖掘)graph mining(图挖掘)graph partitioning 图划分graph query 图查询graph structure(图结构)graph theory(图论)graph theory(图论)graph theory(图论)graph theroy 图论graph visualization(图形可视化)graphical user interface 图形用户界面graphical user interfaces(图形用户界面)health care 卫生保健health care(卫生保健)heterogeneous data source 异构数据源heterogeneous data(异构数据)heterogeneous database 异构数据库heterogeneous information network(异构信息网络) heterogeneous network(异构网络)heterogenous ontology 异构本体heuristic rule 启发式规则hidden markov model(隐马尔可夫模型)hidden markov model(隐马尔可夫模型)hidden markov models(隐马尔可夫模型) hierarchical clustering (层次聚类) homogeneous network(同构网络)human centered computing 人机交互技术human computer interaction 人机交互human interaction 人机交互human robot interaction 人机交互image classification(图像分类)image clustering (图像聚类)image mining( 图像挖掘)image reconstruction(图像重建)image retrieval (图像检索)image segmentation(图像分割)inconsistent ontology 本体不一致incremental learning(增量学习)inductive learning (归纳学习)inference mechanisms 推理机制inference mechanisms(推理机制)inference rule 推理规则information cascades(信息追随)information diffusion(信息扩散)information extraction 信息提取information filtering(信息过滤)information filtering(信息过滤)information integration(信息集成)information network analysis(信息网络分析) information network mining(信息网络挖掘) information network(信息网络)information processing 信息处理information processing 信息处理information resource management (信息资源管理) information retrieval models(信息检索模型) information retrieval 信息检索information retrieval(信息检索)information retrieval(信息检索)information science 情报科学information sources 信息源information system( 信息系统)information system(信息系统)information technology(信息技术)information visualization(信息可视化)instance matching 实例匹配intelligent assistant 智能辅助intelligent systems 智能系统interaction network(交互网络)interactive visualization(交互式可视化)kernel function(核函数)kernel operator (核算子)keyword search(关键字检索)knowledege reuse 知识再利用knowledgeknowledgeknowledge acquisitionknowledge base 知识库knowledge based system 知识系统knowledge building 知识建构knowledge capture 知识获取knowledge construction 知识建构knowledge discovery(知识发现)knowledge extraction 知识提取knowledge fusion 知识融合knowledge integrationknowledge management systems 知识管理系统knowledge management 知识管理knowledge management(知识管理)knowledge model 知识模型knowledge reasoningknowledge representationknowledge representation(知识表达) knowledge sharing 知识共享knowledge storageknowledge technology 知识技术knowledge verification 知识验证language model(语言模型)language modeling approach(语言模型方法) large graph(大图)large graph(大图)learning(无监督学习)life science 生命科学linear programming(线性规划)link analysis (链接分析)link prediction(链接预测)link prediction(链接预测)link prediction(链接预测)linked data(关联数据)location based service(基于位置的服务) loclation based services(基于位置的服务) logic programming 逻辑编程logical implication 逻辑蕴涵logistic regression(logistic 回归)machine learning 机器学习machine translation(机器翻译)management system(管理系统)management( 知识管理)manifold learning(流形学习)markov chains 马尔可夫链markov processes(马尔可夫过程)matching function 匹配函数matrix decomposition(矩阵分解)matrix decomposition(矩阵分解)maximum likelihood estimation(最大似然估计)medical research(医学研究)mixture of gaussians(混合高斯模型)mobile computing(移动计算)multi agnet systems 多智能体系统multiagent systems 多智能体系统multimedia 多媒体natural language processing 自然语言处理natural language processing(自然语言处理) nearest neighbor (近邻)network analysis( 网络分析)network analysis(网络分析)network analysis(网络分析)network formation(组网)network structure(网络结构)network theory(网络理论)network topology(网络拓扑)network visualization(网络可视化)neural network(神经网络)neural networks (神经网络)neural networks(神经网络)nonlinear dynamics(非线性动力学)nonmonotonic reasoning 非单调推理nonnegative matrix factorization (非负矩阵分解) nonnegative matrix factorization(非负矩阵分解) object detection(目标检测)object oriented 面向对象object recognition(目标识别)object recognition(目标识别)online community(网络社区)online social network(在线社交网络)online social networks(在线社交网络)ontology alignment 本体映射ontology development 本体开发ontology engineering 本体工程ontology evolution 本体演化ontology extraction 本体抽取ontology interoperablity 互用性本体ontology language 本体语言ontology mapping 本体映射ontology matching 本体匹配ontology versioning 本体版本ontology 本体论open government data 政府公开数据opinion analysis(舆情分析)opinion mining(意见挖掘)opinion mining(意见挖掘)outlier detection(孤立点检测)parallel processing(并行处理)patient care(病人医疗护理)pattern classification(模式分类)pattern matching(模式匹配)pattern mining(模式挖掘)pattern recognition 模式识别pattern recognition(模式识别)pattern recognition(模式识别)personal data(个人数据)prediction algorithms(预测算法)predictive model 预测模型predictive models(预测模型)privacy preservation(隐私保护)probabilistic logic(概率逻辑)probabilistic logic(概率逻辑)probabilistic model(概率模型)probabilistic model(概率模型)probability distribution(概率分布)probability distribution(概率分布)project management(项目管理)pruning technique(修剪技术)quality management 质量管理query expansion(查询扩展)query language 查询语言query language(查询语言)query processing(查询处理)query rewrite 查询重写question answering system 问答系统random forest(随机森林)random graph(随机图)random processes(随机过程)random walk(随机游走)range query(范围查询)RDF database 资源描述框架数据库RDF query 资源描述框架查询RDF repository 资源描述框架存储库RDF storge 资源描述框架存储real time(实时)recommender system(推荐系统)recommender system(推荐系统)recommender systems 推荐系统recommender systems(推荐系统)record linkage 记录链接recurrent neural network(递归神经网络) regression(回归)reinforcement learning 强化学习reinforcement learning(强化学习)relation extraction 关系抽取relational database 关系数据库relational learning 关系学习relevance feedback (相关反馈)resource description framework 资源描述框架restricted boltzmann machines(受限玻尔兹曼机) retrieval models(检索模型)rough set theroy 粗糙集理论rough set 粗糙集rule based system 基于规则系统rule based 基于规则rule induction (规则归纳)rule learning (规则学习)rule learning 规则学习schema mapping 模式映射schema matching 模式匹配scientific domain 科学域search problems(搜索问题)semantic (web) technology 语义技术semantic analysis 语义分析semantic annotation 语义标注semantic computing 语义计算semantic integration 语义集成semantic interpretation 语义解释semantic model 语义模型semantic network 语义网络semantic relatedness 语义相关性semantic relation learning 语义关系学习semantic search 语义检索semantic similarity 语义相似度semantic similarity(语义相似度)semantic web rule language 语义网规则语言semantic web 语义网semantic web(语义网)semantic workflow 语义工作流semi supervised learning(半监督学习)sensor data(传感器数据)sensor networks(传感器网络)sentiment analysis(情感分析)sentiment analysis(情感分析)sequential pattern(序列模式)service oriented architecture 面向服务的体系结构shortest path(最短路径)similar kernel function(相似核函数)similarity measure(相似性度量)similarity relationship (相似关系)similarity search(相似搜索)similarity(相似性)situation aware 情境感知social behavior(社交行为)social influence(社会影响)social interaction(社交互动)social interaction(社交互动)social learning(社会学习)social life networks(社交生活网络)social machine 社交机器social media(社交媒体)social media(社交媒体)social media(社交媒体)social network analysis 社会网络分析social network analysis(社交网络分析)social network(社交网络)social network(社交网络)social science(社会科学)social tagging system(社交标签系统)social tagging(社交标签)social web(社交网页)sparse coding(稀疏编码)sparse matrices(稀疏矩阵)sparse representation(稀疏表示)spatial database(空间数据库)spatial reasoning 空间推理statistical analysis(统计分析)statistical model 统计模型string matching(串匹配)structural risk minimization (结构风险最小化) structured data 结构化数据subgraph matching 子图匹配subspace clustering(子空间聚类)supervised learning( 有support vector machine 支持向量机support vector machines(支持向量机)system dynamics(系统动力学)tag recommendation(标签推荐)taxonmy induction 感应规范temporal logic 时态逻辑temporal reasoning 时序推理text analysis(文本分析)text anaylsis 文本分析text classification (文本分类)text data(文本数据)text mining technique(文本挖掘技术)text mining 文本挖掘text mining(文本挖掘)text summarization(文本摘要)thesaurus alignment 同义对齐time frequency analysis(时频分析)time series analysis( 时time series data(时间序列数据)time series data(时间序列数据)time series(时间序列)topic model(主题模型)topic modeling(主题模型)transfer learning 迁移学习triple store 三元组存储uncertainty reasoning 不精确推理undirected graph(无向图)unified modeling language 统一建模语言unsupervisedupper bound(上界)user behavior(用户行为)user generated content(用户生成内容)utility mining(效用挖掘)visual analytics(可视化分析)visual content(视觉内容)visual representation(视觉表征)visualisation(可视化)visualization technique(可视化技术) visualization tool(可视化工具)web 2.0(网络2.0)web forum(web 论坛)web mining(网络挖掘)web of data 数据网web ontology lanuage 网络本体语言web pages(web 页面)web resource 网络资源web science 万维科学web search (网络检索)web usage mining(web 使用挖掘)wireless networks 无线网络world knowledge 世界知识world wide web 万维网world wide web(万维网)xml database 可扩展标志语言数据库附录 2 Data Mining 知识图谱（共包含二级节点15 个，三级节点93 个）间序列分析)监督学习)领域 二级分类 三级分类。

超高速基带通信算法英文1. Algorithm -算法2. Ultra-high-speed -超高速3. Baseband -基带4. Communication -通信5. Accurate -准确的6. Answer -回答7. Corresponding -对应的8. English -英文9. Word -单词10. Write -写11. 29 - 2912. Sentence -句子13. Bilingual -双语的14. The algorithm for ultra-high-speed baseband communication is highly efficient.超高速基带通信算法非常高效。

15. Can you accurately answer the corresponding English words?你能准确回答对应的英文单词吗？16. Please write 29 sentences in bilingual.请用双语写29个句子。

17. The development of ultra-high-speed baseband communication algorithms has revolutionized the field of telecommunications.超高速基带通信算法的发展彻底改变了电信领域。

18. We need an accurate algorithm to optimize the baseband communication system.我们需要一个准确的算法来优化基带通信系统。

19. Do you have the answer to the corresponding English word for "communication"?你对于"communication"这个对应的英文单词有答案吗？20. The English word for "algorithm" is the same in both languages."algorithm"这个英文单词在两种语言中都一样。

dtnl练习题（打印版）# DTNL 练习题（打印版）## 一、选择题1. 下列哪个选项不是深度学习（Deep Learning, DL）的典型应用？- A. 图像识别- B. 自然语言处理- C. 线性回归- D. 神经网络2. 在深度学习中，以下哪个术语与反向传播算法（Backpropagation）无关？- A. 梯度下降- B. 损失函数- C. 卷积神经网络- D. 特征提取## 二、填空题1. 深度学习模型中的激活函数通常用于引入________，以帮助模型学习复杂的数据模式。

2. 卷积神经网络（CNN）中的卷积层主要用于提取图像的________特征。

3. 在训练深度学习模型时，________是用于评估模型在训练集上的性能的指标。

## 三、简答题1. 简要描述什么是深度学习，并说明它与传统机器学习方法的主要区别。

2. 解释什么是过拟合（Overfitting），并给出避免过拟合的几种策略。

## 四、计算题给定一个简单的神经网络，包含一个输入层，两个隐藏层和一个输出层。

假设输入层有4个神经元，第一个隐藏层有8个神经元，第二个隐藏层有6个神经元，输出层有3个神经元。

如果输入层的激活值为[0.2, 0.5, 0.8, 1.0]，第一个隐藏层的权重矩阵为：\[W_1 =\begin{bmatrix}0.1 & 0.2 & 0.3 & 0.4 \\0.5 & 0.6 & 0.7 & 0.8 \\0.9 & 1.0 & 1.1 & 1.2 \\\end{bmatrix}\]第一个隐藏层的偏置向量为 \( b_1 = [0.1, 0.2, 0.3, 0.4] \)，激活函数为 ReLU。

请计算第一个隐藏层的输出激活值。

## 五、编程题编写一个简单的 Python 函数，该函数接受一个列表作为输入，返回列表中所有元素的和。

```pythondef sum_elements(input_list):# 你的代码pass```## 六、案例分析题考虑一个实际问题，例如图像识别、语音识别或自然语言处理等，描述如何使用深度学习技术来解决这个问题，并简要说明所选择的模型架构和训练过程。

《基于上下文感知及边界引导的伪装物体检测研究》篇一一、引言随着科技的发展，伪装物体检测在众多领域中发挥着越来越重要的作用，如安全监控、军事侦察和图像处理等。

由于伪装物体的复杂性及背景环境的多样性，传统基于特征的检测方法已无法满足精确度和效率的要求。

因此，本研究基于上下文感知及边界引导，提出了改进的伪装物体检测算法。

该算法可以有效地从复杂的背景中识别出伪装物体，为相关领域提供更准确的检测结果。

二、上下文感知在伪装物体检测中的应用上下文感知是指利用物体与其周围环境的关系进行识别和检测。

在伪装物体检测中，上下文感知的应用主要体现在对物体与周围环境的关联性分析。

通过分析物体的形状、大小、颜色、纹理等特征与周围环境的相互关系，我们可以更好地理解物体在场景中的位置和作用，从而准确判断其是否为伪装物体。

我们采用了基于区域的方法进行上下文感知的建模。

首先，对图像进行分块处理，提取出各个区域内的特征。

然后，通过分析不同区域之间的特征关系，建立上下文模型。

最后，利用该模型对图像进行分类和识别，从而实现对伪装物体的检测。

三、边界引导在伪装物体检测中的作用边界引导是指利用图像中的边缘信息对物体进行定位和识别。

在伪装物体检测中，边界引导的作用主要体现在对物体边缘的精确提取和识别。

通过分析物体的边缘特征，我们可以更准确地判断其形状和位置，从而实现对伪装物体的精确检测。

我们采用了基于边缘检测的方法进行边界引导的实现。

首先，对图像进行预处理，增强边缘信息。

然后，利用边缘检测算法提取出图像中的边缘特征。

最后，结合上下文感知的结果，对边缘特征进行进一步的分析和处理，从而实现对伪装物体的精确检测。

四、实验与分析为了验证本算法的有效性，我们进行了大量的实验。

实验结果表明，基于上下文感知及边界引导的伪装物体检测算法具有较高的准确性和稳定性。

与传统的基于特征的检测方法相比，本算法在处理复杂背景和多变环境下的伪装物体检测问题时表现出更强的鲁棒性。

2023年《人工智能》现代科技知识考试题与答案目录简介一、单选题：共40题二、多选题：共20题三、判断题：共26题一、单选题1、下列哪部分不是专家系统的组成部分？A .用户B.综合数据库C.推理机D.知识库正确答案：A解析：《人工智能导论》（第4版）作者：王万良出版社: 高等教育出版社2、下列哪个神经网络结构会发生权重共享？A.卷积神经网络B.循环神经网络C.全连接神经网络D. A 和B正确答案：D解析：《深度学习、优化与识别》作者:焦李成出版社: 清华大学出版社3、下列哪个不属于常用的文本分类的特征选择算法？A.卡方检验值B.互信息C .信息增益D.主成分分析正确答案：D解析：《自然语言处理》作者：刘挺出版社：高等教育出版社4、下列哪个不是人工智能的技术应用领域？A.搜索技术B.数据挖掘C.智能控制D .编译原理解析：《走进人工智能》作者：周旺出版社：高等教育出版社5、Q（s,a）是指在给定状态s的情况下，采取行动a之后，后续的各个状态所能得到的回报（）。

A.总和B.最大值C.最小值D.期望值正确答案：D解析：《深度学习、优化与识别》作者:焦李成出版社: 清华大学出版社6、数据科学家可能会同时使用多个算法（模型）进行预测，并且最后把这些算法的结果集成起来进行最后的预测（集成学习），以下对集成学习说法正确的是（）。

A.单个模型之间有高相关性B.单个模型之间有低相关性C,在集成学习中使用“平均权重”而不是“投票”会比较好D.单个模型都是用的一个算法解析：《机器学习方法》作者：李航出版社：清华大学出版社7、以下哪种技术对于减少数据集的维度会更好？A.删除缺少值太多的列B.删除数据差异较大的列C.删除不同数据趋势的列D.都不是正确答案：A解析：《机器学习》作者：周志华出版社：清华大学出版社8、在强化学习过程中，学习率越大，表示采用新的尝试得到的结果比例越（），保持旧的结果的比例越（）。

A .大，小B.大,大C.小，大D.小，小正确答案：A解析：《深度学习、优化与识别》作者:焦李成出版社: 清华大学出版社9、以下哪种方法不属于特征选择的标准方法？A.嵌入B.过滤C ,包装D.抽样正确答案：D解析：《深度学习、优化与识别》作者:焦李成出版社: 清华大学出版社10、要想让机器具有智能，必须让机器具有知识。

一、选择题1.在机器学习中，哪种算法常用于分类问题，通过构建决策树来进行预测？A.线性回归B.决策树算法（答案）C.K-means聚类D.主成分分析2.下列哪一项不是深度学习的常用框架？A.TensorFlowB.PyTorchC.Scikit-learn（答案）D.Keras3.在神经网络中，权重和偏置的初始值设置对训练过程有很大影响。

哪种初始化方法可以帮助避免梯度消失或爆炸问题？A.随机初始化B.Xavier/Glorot初始化（答案）C.全零初始化D.全一初始化4.以下哪项技术常用于自然语言处理（NLP）中的词嵌入表示？A.词袋模型B.TF-IDFC.Word2Vec（答案）D.One-hot编码5.在强化学习中，智能体（Agent）根据什么来选择动作？A.奖励函数（答案）B.状态转移概率C.动作空间大小D.环境模型6.下列哪一项不是卷积神经网络（CNN）的常用层？A.卷积层B.池化层C.全连接层D.循环层（答案）7.在机器学习的模型评估中，哪种方法可以用于评估分类模型的性能，通过计算真正例、假正例、真反例和假反例的数量？A.混淆矩阵（答案）B.ROC曲线C.交叉验证D.准确率8.以下哪个算法是基于实例的学习算法，通过计算新数据与训练集中每个数据点的相似度来进行分类或回归？A.K-近邻算法（答案）B.支持向量机C.神经网络D.决策树9.在自然语言处理中，哪种技术可以用于将句子或文档转换为固定长度的向量表示？A.词嵌入B.文本分类C.情感分析D.Sentence Embedding（答案）10.以下哪个术语用于描述在训练过程中，模型在训练集上的性能逐渐提高，但在测试集上的性能开始下降的现象？A.过拟合（答案）B.欠拟合C.交叉验证D.泛化能力。

参考文献(人工智能)曹晖目的：对参考文献整理（包括摘要、读书笔记等），方便以后的使用。

分类：粗分为论文（paper）、教程（tutorial）和文摘（digest）。

0介绍 (1)1系统与综述 (1)2神经网络 (2)3机器学习 (2)3.1联合训练的有效性和可用性分析 (2)3.2文本学习工作的引导 (2)3.3★采用机器学习技术来构造受限领域搜索引擎 (3)3.4联合训练来合并标识数据与未标识数据 (5)3.5在超文本学习中应用统计和关系方法 (5)3.6在关系领域发现测试集合规律性 (6)3.7网页挖掘的一阶学习 (6)3.8从多语种文本数据库中学习单语种语言模型 (6)3.9从因特网中学习以构造知识库 (7)3.10未标识数据在有指导学习中的角色 (8)3.11使用增强学习来有效爬行网页 (8)3.12★文本学习和相关智能A GENTS：综述 (9)3.13★新事件检测和跟踪的学习方法 (15)3.14★信息检索中的机器学习——神经网络，符号学习和遗传算法 (15)3.15用NLP来对用户特征进行机器学习 (15)4模式识别 (16)4.1JA VA中的模式处理 (16)0介绍1系统与综述2神经网络3机器学习3.1 联合训练的有效性和可用性分析标题：Analyzing the Effectiveness and Applicability of Co-training链接：Papers 论文集\AI 人工智能\Machine Learning 机器学习\Analyzing the Effectiveness and Applicability of Co-training.ps作者：Kamal Nigam, Rayid Ghani备注：Kamal Nigam (School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, knigam@)Rayid Ghani (School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213 rayid@)摘要：Recently there has been significant interest in supervised learning algorithms that combine labeled and unlabeled data for text learning tasks. The co-training setting [1] applies todatasets that have a natural separation of their features into two disjoint sets. We demonstrate that when learning from labeled and unlabeled data, algorithms explicitly leveraging a natural independent split of the features outperform algorithms that do not. When a natural split does not exist, co-training algorithms that manufacture a feature split may out-perform algorithms not using a split. These results help explain why co-training algorithms are both discriminativein nature and robust to the assumptions of their embedded classifiers.3.2 文本学习工作的引导标题：Bootstrapping for Text Learning Tasks链接：Papers 论文集\AI 人工智能\Machine Learning 机器学习\Bootstrap for Text Learning Tasks.ps作者：Rosie Jones, Andrew McCallum, Kamal Nigam, Ellen Riloff备注：Rosie Jones (rosie@, 1 School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213)Andrew McCallum (mccallum@, 2 Just Research, 4616 Henry Street, Pittsburgh, PA 15213)Kamal Nigam (knigam@)Ellen Riloff (riloff@, Department of Computer Science, University of Utah, Salt Lake City, UT 84112)摘要：When applying text learning algorithms to complex tasks, it is tedious and expensive to hand-label the large amounts of training data necessary for good performance. This paper presents bootstrapping as an alternative approach to learning from large sets of labeled data. Instead of a large quantity of labeled data, this paper advocates using a small amount of seed information and alarge collection of easily-obtained unlabeled data. Bootstrapping initializes a learner with the seed information; it then iterates, applying the learner to calculate labels for the unlabeled data, and incorporating some of these labels into the training input for the learner. Two case studies of this approach are presented. Bootstrapping for information extraction provides 76% precision for a 250-word dictionary for extracting locations from web pages, when starting with just a few seed locations. Bootstrapping a text classifier from a few keywords per class and a class hierarchy provides accuracy of 66%, a level close to human agreement, when placing computer science research papers into a topic hierarchy. The success of these two examples argues for the strength of the general bootstrapping approach for text learning tasks.3.3 ★采用机器学习技术来构造受限领域搜索引擎标题：Building Domain-specific Search Engines with Machine Learning Techniques链接：Papers 论文集\AI 人工智能\Machine Learning 机器学习\Building Domain-Specific Search Engines with Machine Learning Techniques.ps作者：Andrew McCallum, Kamal Nigam, Jason Rennie, Kristie Seymore备注：Andrew McCallum (mccallum@ , Just Research, 4616 Henry Street Pittsburgh, PA 15213)Kamal Nigam (knigam@ , School of Computer Science, Carnegie Mellon University Pittsburgh, PA 15213)Jason Rennie (jr6b@)Kristie Seymore (kseymore@)摘要：Domain-specific search engines are growing in popularity because they offer increased accuracy and extra functionality not possible with the general, Web-wide search engines. For example, allows complex queries by age-group, size, location and cost over summer camps. Unfortunately these domain-specific search engines are difficult and time-consuming to maintain. This paper proposes the use of machine learning techniques to greatly automate the creation and maintenance of domain-specific search engines. We describe new research in reinforcement learning, information extraction and text classification that enables efficient spidering, identifying informative text segments, and populating topic hierarchies. Using these techniques, we have built a demonstration system: a search engine forcomputer science research papers. It already contains over 50,000 papers and is publicly available at ....采用多项Naive Bayes 文本分类模型。

2024年华为人工智能方向HCIA考试复习题库（含答案）一、单选题1.以下哪—项不属于MindSpore全场景部署和协同的关键特性?A、统一模型R带来一致性的部署体验。

B、端云协同FederalMetaLearning打破端云界限,多设备协同模型。

C、数据+计算整图到Ascend芯片。

D、软硬协同的图优化技术屏蔽场景差异。

参考答案：C2.在对抗生成网络当中,带有标签的数据应该被放在哪里?A、作为生成模型的输出值B、作为判别模型的输入值C、作为判别模型的输出值D、作为生成模型的输入值参考答案：B3.下列属性中TensorFlow2.0不支持创建tensor的方法是?A、zerosB、fillC、createD、constant参考答案：C4.以下哪一项是HiAI3.0相对于2.0提升的特点？A、单设备B、分布式C、多设备D、端云协同参考答案：B5.以下哪个不是MindSpore中Tensor常见的操作？A、asnumpy()B、dim()C、for()D、size()参考答案：C6.优化器是训练神经网络的重要组成部分,使用优化器的目的不包含以下哪项:A、加快算法收敛速度B、减少手工参数的设置难度C、避过过拟合问题D、避过局部极值参考答案：C7.K折交叉验证是指将测试数据集划分成K个子数据集。

A、TRUEB、FALSE参考答案：B8.机器学习是深度学习的一部分。

人工智能也是深度学习的一部分。

A、TrueB、False参考答案：B9.在神经网络中,我们是通过以下哪个方法在训练网络的时候更新参数,从而最小化损失函数的?A、正向传播算法B、池化计算C、卷积计算D、反向传播算法参考答案：D10.以下不属于TensorFlow2.0的特点是?A、多核CPU加速B、分布式C、多语言D、多平台参考答案：A11.以下关于机器学习中分类模型与回归模型的说法,哪一项说法是正确的?A、对回归问题和分类问题的评价,最常用的指标都是准确率和召回率B、输出变量为有限个离散变量的预测问题是回归问题,输出变量为连续变量的预测问题是分类问题C、回归问题知分类问题都有可能发生过拟合D、逻辑回归是一种典型的回归模型参考答案：C12.ModelArts平台中的数据管理中不支持视频数据格式。

光学 精密工程Optics and Precision Engineering第 29 卷 第 5 期2021年5月Vol. 29 No. 5May 2021文章编号 1004-924X( 2021)05-1127-09联合训练生成对抗网络的半监督分类方法徐哲，耿杰*，蒋雯，张卓，曾庆捷(西北工业大学电子信息学院，西安710072)摘要：深度神经网络需要大量数据进行监督训练学习，而实际应用中往往难以获取大量标签数据°半监督学习可以减小深度网络对标签数据的依赖，基于半监督学习的生成对抗网络可以提升分类效果,旦仍存在训练不稳定的问题°为进一步提高网络的分类精度并解决网络训练不稳定的问题，本文提出一种基于联合训练生成对抗网络的半监督分类方法，通 过两个判别器的联合训练来消除单个判别器的分布误差，同时选取无标签数据中置信度高的样本来扩充标签数据集，提高半监督分类精度并提升网络模型的泛化能力°在CIFAR -10和SVHN 数据集上的实验结果表明，本文方法在不同数量的标签数据下都获得更好的分类精度°当标签数量为2 000时，在CIFAR -10数据集上分类精度可达80.36% ;当标签 数量为10时，相比于现有的半监督方法，分类精度提升了约5%°在一定程度上解决了 GAN 网络在小样本条件下的过拟合问题°关键词：生成对抗网络；半监督学习；图像分类；深度学习中图分类号:TP391文献标识码：Adoi ：10. 37188/OPE. 20212905.1127Co -training generative adversarial networks forsemi -supervised classification methodXU Zhe , GENG Jie * , JIANG Wen , ZHANG Zhuo , ZENG Qing -jie(School of E lectronics and Information , Northwestern Polytechnical University , Xian 710072, China )* Corresponding author , E -mail ： gengjie@nwpu. edu. cnAbstract ： Deep neural networks require a large amount of data for supervised learning ； however , it is dif ­ficult to obtain enough labeled data in practical applications. Semi -supervised learning can train deep neuralnetworks with limited samples. Semi -supervised generative adversarial networks can yield superior classifi ­cation performance ； however ， they are unstable during training in classical networks. To further improve the classification accuracy and solve the problem of training instability for networks ， we propose a semi -su ­pervised classification model called co -training generative adversarial networks ( CT -GAN ) for image clas ­sification. In the proposed model ， co -training of two discriminators is applied to eliminate the distribution error of a single discriminator and unlabeled samples with higher confidence are selected to expand thetraining set , which can be utilized for semi -supervised classification and enhance the generalization of deep networks. Experimental results on the CIFAR -10 dataset and the SVHN dataset showed that the pro ­posed method achieved better classification accuracies with different numbers of labeled data. The classifi ­cation accuracy was 80. 36% with 2000 labeled data on the CIFAR -10 dataset , whereas it improved by收稿日期：2020-11-04；修订日期:2021-01-04.基金项目:装备预研领域基金资助项目(No. 61400010304);国家自然科学基金资助项目(No. 61901376)1128光学精密工程第29卷about5%compared with the existing semi-supervised method with10labeled data.To a certain extent, the problem of GAN overfitting under a few sample conditions is solved.Key words：generative adversarial networks；semi-supervised learning；image classification；deep learn­ing1引言图像分类作为计算机视觉领域最基础的任务之一，主要通过提取原始图像的特征并根据特征学习进行分类［11o传统的特征提取方法主要是对图像的颜色、纹理、局部特征等图像表层特征进行处理实现的，例如尺度不变特征变换法［21,方向梯度法［31以及局部二值法［41等。

danswer原理
Danswer是一个创新的信息检索工具，它能够让你针对内部文档提出自然语言问题，并获取到源材料中引用和参考的可靠答案。

其原理如下：
当用户提交查询时，首先通过用户意图模型进行处理，该模型确定是使用关键字搜索还是语义搜索。

语义搜索流程分为两个步骤：检索和重新排序。

检索是通过使用双编码器模型对文本进行嵌入来完成的。

向量存储在Qdrant向量数据库中，然后在查询时通过以相同方式投影用户查询来获取。

在进行检索时会执行一些操作，包括上下文感知分块文档，包括跨块的重叠，并在不同尺度上多次嵌入块。

一次使用512个标记进行更大的上下文，然后使用128个标记进行更精细的细节。

对于重新排序，使用了一组不同的模型作为集合。

通过组合在不同数据集上表现更好的多个模型，可以获得最佳的重新排序结果，甚至可以使用较小的模型，从而使步骤整体更快。

最后，最相关的文档部分被传递给生成模型，该模型被提示也提供其答案的引用。

然后将引用与源文档匹配，并将其与答案一起呈现给用户。

精选全文完整版（可编辑修改）人工智能导论测试题库及答案1、在关联规则分析过程中，对原始数据集进行事务型数据处理的主要原因是。

A、提高数据处理速度B、节省存储空间C、方便算法计算D、形成商品交易矩阵答案：C2、计算机视觉可应用于下列哪些领域()。

A、安防及监控领域B、金融领域的人脸识别身份验证C、医疗领域的智能影像诊断D、机器人/无人车上作为视觉输入系统E、以上全是答案：E3、1943年，神经网络的开山之作《A logical calculus of ideas immanent in nervous activity》,由()和沃尔特.皮茨完成。

A、沃伦.麦卡洛克B、明斯基C、唐纳德.赫布D、罗素答案：A4、对于自然语言处理问题，哪种神经网络模型结构更适合?()。

A、多层感知器B、卷积神经网络C、循环神经网络D、感知器答案：C5、图像的空间离散化叫做：A、灰度化B、二值化C、采样D、量化答案：C6、()越多，所得图像层次越丰富，灰度分辨率高，图像质量好。

A、分辨率B、像素数量C、量化等级D、存储的数据量答案：C7、一个完整的人脸识别系统主要包含人脸图像采集和检测、人脸图像特征提取和人脸识别四个部分。

A、人脸分类器B、人脸图像预处理C、人脸数据获取D、人脸模型训练答案：B8、下列不属于人工智能学派的是()。

A、符号主义B、连接主义C、行为主义D、机会主义答案：D9、关于正负样本的说法正确是。

A、样本数量多的那一类是正样本B、样本数量少的那一类是负样本C、正负样本没有明确的定义D、想要正确识别的那一类为正样本答案：D10、以下不属于完全信息博弈的游戏有()。

A、井字棋B、黑白棋C、围棋D、桥牌答案：D11、下列关于人工智能的说法中，哪一项是错误的。

A、人工智能是一门使机器做那些人需要通过智能来做的事情的学科B、人工智能主要研究知识的表示、知识的获取和知识的运用C、人工智能是研究机器如何像人一样合理思考、像人一样合理行动的学科D、人工智能是研究机器如何思维的一门学科答案：D12、认为智能不需要知识、不需要表示、不需要推理；人工智能可以像人类智能一样逐步进化；智能行为只能在现实世界中与周围环境交互作用而表现出来。

moco 特征提取
MoCo（Momentum Contrast）是一种无监督表征学习的方法，利用对比学习训练特征提取器。

这种方法的核心思想是将对比学习视为字典查询任务，并建立了一个动态字典，该字典由队列和移动平均编码器组成。

在MoCo的训练过程中，队列中的样本不需要进行梯度回传，因此可以在队列中存放大量的负样本，从而使字典变得很大。

移动平均编码器的目的是保持字典中的特征尽可能一致，这对于无监督对比学习是非常有益的。

使用MoCo训练出的特征提取器在下游任务中表现优异。

在ImageNet的分类任务上，使用MoCo作为特征提取器获得了与之前的无监督方法相当的效果。

此外，在7个检测/分割下游任务上，使用预训练的MoCo甚至取得了比有监督学习更好的效果，有时甚至是大幅度超越。

总的来说，MoCo是一种有效的无监督特征提取方法，通过构建大的、一致的字典来实现优秀的特征提取性能。

最相似近邻法-概述说明以及解释1.引言1.1 概述最相似近邻法是一种常用的机器学习算法，也被称为k近邻算法。

它是一种基于实例的学习方法，通过计算待预测样本与训练集中样本的相似度，来进行分类或回归预测。

该算法的核心思想是利用输入样本与训练集中已有样本的特征信息进行对比，找出与输入样本最相似的k个样本，并根据它们的标签信息来对输入样本进行分类或回归预测。

这种基于相似度的方法能够很好地捕捉样本之间的关系，适用于各种不规则分布的数据集。

最相似近邻法在实际应用中具有广泛的适用性，包括图像识别、推荐系统、医学诊断等领域。

尽管该算法存在一定的计算复杂度和需要大量存储空间的缺点，但其简单直观的原理和良好的泛化能力使其成为机器学习领域中不可或缺的一部分。

1.2 文章结构本文分为引言、正文和结论三个部分。

在引言部分，将对最相似近邻法进行概述，并介绍文章的结构和目的。

在正文部分，将详细介绍什么是最相似近邻法，以及它在不同应用领域的具体应用情况。

同时，将梳理最相似近邻法的优缺点，为读者提供全面的了解。

最后，在结论部分，将总结本文的主要内容，展望最相似近邻法的未来发展前景，并给出结论性的观点和建议。

整个文章将通过逻辑清晰的结构，带领读者深入理解和认识最相似近邻法的重要性和应用。

1.3 目的最相似近邻法是一种常用的机器学习算法，其主要目的是通过比较不同数据点之间的相似度，找出与目标数据点最相似的邻居。

通过这种方法，我们可以实现数据分类、推荐系统、图像识别等多种应用。

本文旨在深入探讨最相似近邻法的原理、应用领域以及优缺点，希望读者能更全面地了解这一算法，并在实际应用中取得更好的效果。

同时，我们也将展望最相似近邻法在未来的发展前景，为读者提供对未来研究方向的参考。

通过本文的阐述，希望读者能够更深入地理解最相似近邻法，为其在实际应用中提供更好的指导。

2.正文2.1 什么是最相似近邻法最相似近邻法是一种常用的机器学习算法，它通过计算数据样本之间的相似度来进行分类或回归预测。

•Creating a reference list or bibliographyA numbered list of references must be provided at the end of thepaper. The list should be arranged in the order of citation in the text of the assignment or essay, not in alphabetical order. List only one reference per reference number. Footnotes or otherinformation that are not part of the referencing format should not be included in the reference list.The following examples demonstrate the format for a variety of types of references. Included are some examples of citing electronic documents. Such items come in many forms, so only some examples have been listed here.Print DocumentsBooksNote: Every (important) word in the title of a book or conference must be capitalised. Only the first word of a subtitle should be capitalised. Capitalise the "v" in Volume for a book title.Punctuation goes inside the quotation marks.Standard formatSingle author[1] W.-K. Chen, Linear Networks and Systems. Belmont, CA: Wadsworth,1993, pp. 123-135.[2] S. M. Hemmington, Soft Science. Saskatoon: University ofSaskatchewan Press, 1997.Edited work[3] D. Sarunyagate, Ed., Lasers. New York: McGraw-Hill, 1996.Later edition[4] K. Schwalbe, Information Technology Project Management, 3rd ed.Boston: Course Technology, 2004.[5] M. N. DeMers, Fundamentals of Geographic Information Systems,3rd ed. New York : John Wiley, 2005.More than one author[6] T. Jordan and P. A. Taylor, Hacktivism and Cyberwars: Rebelswith a cause? London: Routledge, 2004.[7] U. J. Gelinas, Jr., S. G. Sutton, and J. Fedorowicz, Businessprocesses and information technology. Cincinnati:South-Western/Thomson Learning, 2004.Three or more authorsNote: The names of all authors should be given in the references unless the number of authors is greater than six. If there are more than six authors, you may use et al. after the name of the first author.[8] R. Hayes, G. Pisano, D. Upton, and S. Wheelwright, Operations,Strategy, and Technology: Pursuing the competitive edge.Hoboken, NJ : Wiley, 2005.Series[9] M. Bell, et al., Universities Online: A survey of onlineeducation and services in Australia, Occasional Paper Series 02-A. Canberra: Department of Education, Science andTraining, 2002.Corporate author (ie: a company or organisation)[10] World Bank, Information and Communication Technologies: AWorld Bank group strategy. Washington, DC : World Bank, 2002.Conference (complete conference proceedings)[11] T. J. van Weert and R. K. Munro, Eds., Informatics and theDigital Society: Social, ethical and cognitive issues: IFIP TC3/WG3.1&3.2 Open Conference on Social, Ethical andCognitive Issues of Informatics and ICT, July 22-26, 2002, Dortmund, Germany. Boston: Kluwer Academic, 2003.Government publication[12] Australia. Attorney-Generals Department. Digital AgendaReview, 4 Vols. Canberra: Attorney- General's Department,2003.Manual[13] Bell Telephone Laboratories Technical Staff, TransmissionSystem for Communications, Bell Telephone Laboratories,1995.Catalogue[14] Catalog No. MWM-1, Microwave Components, M. W. Microwave Corp.,Brooklyn, NY.Application notes[15] Hewlett-Packard, Appl. Note 935, pp. 25-29.Note:Titles of unpublished works are not italicised or capitalised. Capitalise only the first word of a paper or thesis.Technical report[16] K. E. Elliott and C.M. Greene, "A local adaptive protocol,"Argonne National Laboratory, Argonne, France, Tech. Rep.916-1010-BB, 1997.Patent / Standard[17] K. Kimura and A. Lipeles, "Fuzzy controller component, " U.S. Patent 14,860,040, December 14, 1996.Papers presented at conferences (unpublished)[18] H. A. Nimr, "Defuzzification of the outputs of fuzzycontrollers," presented at 5th International Conference onFuzzy Systems, Cairo, Egypt, 1996.Thesis or dissertation[19] H. Zhang, "Delay-insensitive networks," M.S. thesis,University of Waterloo, Waterloo, ON, Canada, 1997.[20] M. W. Dixon, "Application of neural networks to solve therouting problem in communication networks," Ph.D.dissertation, Murdoch University, Murdoch, WA, Australia, 1999.Parts of a BookNote: These examples are for chapters or parts of edited works in which the chapters or parts have individual title and author/s, but are included in collections or textbooks edited by others. If the editors of a work are also the authors of all of the included chapters then it should be cited as a whole book using the examples given above (Books).Capitalise only the first word of a paper or book chapter.Single chapter from an edited work[1] A. Rezi and M. Allam, "Techniques in array processing by meansof transformations, " in Control and Dynamic Systems, Vol.69, Multidemsional Systems, C. T. Leondes, Ed. San Diego: Academic Press, 1995, pp. 133-180.[2] G. O. Young, "Synthetic structure of industrial plastics," inPlastics, 2nd ed., vol. 3, J. Peters, Ed. New York:McGraw-Hill, 1964, pp. 15-64.Conference or seminar paper (one paper from a published conference proceedings)[3] N. Osifchin and G. Vau, "Power considerations for themodernization of telecommunications in Central and Eastern European and former Soviet Union (CEE/FSU) countries," in Second International Telecommunications Energy SpecialConference, 1997, pp. 9-16.[4] S. Al Kuran, "The prospects for GaAs MESFET technology in dc-acvoltage conversion," in Proceedings of the Fourth AnnualPortable Design Conference, 1997, pp. 137-142.Article in an encyclopaedia, signed[5] O. B. R. Strimpel, "Computer graphics," in McGraw-HillEncyclopedia of Science and Technology, 8th ed., Vol. 4. New York: McGraw-Hill, 1997, pp. 279-283.Study Guides and Unit ReadersNote: You should not cite from Unit Readers, Study Guides, or lecture notes, but where possible you should go to the original source of the information. If you do need to cite articles from the Unit Reader, treat the Reader articles as if they were book or journal articles. In the reference list or bibliography use the bibliographical details as quoted in the Reader and refer to the page numbers from the Reader, not the original page numbers (unless you have independently consulted the original).[6] L. Vertelney, M. Arent, and H. Lieberman, "Two disciplines insearch of an interface: Reflections on a design problem," in The Art of Human-Computer Interface Design, B. Laurel, Ed.Reading, MA: Addison-Wesley, 1990. Reprinted inHuman-Computer Interaction (ICT 235) Readings and Lecture Notes, Vol. 1. Murdoch: Murdoch University, 2005, pp. 32-37. Journal ArticlesNote: Capitalise only the first word of an article title, except for proper nouns or acronyms. Every (important) word in the title of a journal must be capitalised. Do not capitalise the "v" in volume for a journal article.You must either spell out the entire name of each journal that you reference or use accepted abbreviations. You must consistently do one or the other. Staff at the Reference Desk can suggest sources of accepted journal abbreviations.You may spell out words such as volume or December, but you must either spell out all such occurrences or abbreviate all. You do not need to abbreviate March, April, May, June or July.To indicate a page range use pp. 111-222. If you refer to only one page, use only p. 111.Standard formatJournal articles[1] E. P. Wigner, "Theory of traveling wave optical laser," Phys.Rev., vol. 134, pp. A635-A646, Dec. 1965.[2] J. U. Duncombe, "Infrared navigation - Part I: An assessmentof feasability," IEEE Trans. Electron. Devices, vol. ED-11, pp. 34-39, Jan. 1959.[3] G. Liu, K. Y. Lee, and H. F. Jordan, "TDM and TWDM de Bruijnnetworks and shufflenets for optical communications," IEEE Trans. Comp., vol. 46, pp. 695-701, June 1997.OR[4] J. R. Beveridge and E. M. Riseman, "How easy is matching 2D linemodels using local search?" IEEE Transactions on PatternAnalysis and Machine Intelligence, vol. 19, pp. 564-579, June 1997.[5] I. S. Qamber, "Flow graph development method," MicroelectronicsReliability, vol. 33, no. 9, pp. 1387-1395, Dec. 1993.[6] E. H. Miller, "A note on reflector arrays," IEEE Transactionson Antennas and Propagation, to be published.Electronic documentsNote:When you cite an electronic source try to describe it in the same way you would describe a similar printed publication. If possible, give sufficient information for your readers to retrieve the source themselves.If only the first page number is given, a plus sign indicates following pages, eg. 26+. If page numbers are not given, use paragraph or other section numbers if you need to be specific. An electronic source may not always contain clear author or publisher details.The access information will usually be just the URL of the source. As well as a publication/revision date (if there is one), the date of access is included since an electronic source may change between the time you cite it and the time it is accessed by a reader.E-BooksStandard format[1] L. Bass, P. Clements, and R. Kazman. Software Architecture inPractice, 2nd ed. Reading, MA: Addison Wesley, 2003. [E-book] Available: Safari e-book.[2] T. Eckes, The Developmental Social Psychology of Gender. MahwahNJ: Lawrence Erlbaum, 2000. [E-book] Available: netLibrary e-book.Article in online encyclopaedia[3] D. Ince, "Acoustic coupler," in A Dictionary of the Internet.Oxford: Oxford University Press, 2001. [Online]. Available: Oxford Reference Online, .[Accessed: May 24, 2005].[4] W. D. Nance, "Management information system," in The BlackwellEncyclopedic Dictionary of Management Information Systems,G.B. Davis, Ed. Malden MA: Blackwell, 1999, pp. 138-144.[E-book]. Available: NetLibrary e-book.E-JournalsStandard formatJournal article abstract accessed from online database[1] M. T. Kimour and D. Meslati, "Deriving objects from use casesin real-time embedded systems," Information and SoftwareTechnology, vol. 47, no. 8, p. 533, June 2005. [Abstract].Available: ProQuest, /proquest/.[Accessed May 12, 2005].Note: Abstract citations are only included in a reference list if the abstract is substantial or if the full-text of the article could not be accessed.Journal article from online full-text databaseNote: When including the internet address of articles retrieved from searches in full-text databases, please use the Recommended URLs for Full-text Databases, which are the URLs for the main entrance to the service and are easier to reproduce.[2] H. K. Edwards and V. Sridhar, "Analysis of software requirementsengineering exercises in a global virtual team setup,"Journal of Global Information Management, vol. 13, no. 2, p.21+, April-June 2005. [Online]. Available: Academic OneFile, . [Accessed May 31, 2005].[3] A. Holub, "Is software engineering an oxymoron?" SoftwareDevelopment Times, p. 28+, March 2005. [Online]. Available: ProQuest, . [Accessed May 23, 2005].Journal article in a scholarly journal (published free of charge on the internet)[4] A. Altun, "Understanding hypertext in the context of readingon the web: Language learners' experience," Current Issues in Education, vol. 6, no. 12, July 2003. [Online]. Available: /volume6/number12/. [Accessed Dec. 2, 2004].Journal article in electronic journal subscription[5] P. H. C. Eilers and J. J. Goeman, "Enhancing scatterplots withsmoothed densities," Bioinformatics, vol. 20, no. 5, pp.623-628, March 2004. [Online]. Available:. [Accessed Sept. 18, 2004].Newspaper article from online database[6] J. Riley, "Call for new look at skilled migrants," TheAustralian, p. 35, May 31, 2005. Available: Factiva,. [Accessed May 31, 2005].Newspaper article from the Internet[7] C. Wilson-Clark, "Computers ranked as key literacy," The WestAustralian, para. 3, March 29, 2004. [Online]. Available:.au. [Accessed Sept. 18, 2004].Internet DocumentsStandard formatProfessional Internet site[1] European Telecommunications Standards Institute, 揇igitalVideo Broadcasting (DVB): Implementation guidelines for DVBterrestrial services; transmission aspects,?EuropeanTelecommunications Standards Institute, ETSI TR-101-190,1997. [Online]. Available: . [Accessed:Aug. 17, 1998].Personal Internet site[2] G. Sussman, "Home page - Dr. Gerald Sussman," July 2002.[Online]. Available:/faculty/Sussman/sussmanpage.htm[Accessed: Sept. 12, 2004].General Internet site[3] J. Geralds, "Sega Ends Production of Dreamcast," ,para. 2, Jan. 31, 2001. [Online]. Available:/news/1116995. [Accessed: Sept. 12,2004].Internet document, no author given[4] 揂憀ayman抯?explanation of Ultra Narrow Band technology,?Oct.3, 2003. [Online]. Available:/Layman.pdf. [Accessed: Dec. 3, 2003].Non-Book FormatsPodcasts[1] W. Brown and K. Brodie, Presenters, and P. George, Producer, 揊rom Lake Baikal to the Halfway Mark, Yekaterinburg? Peking to Paris: Episode 3, Jun. 4, 2007. [Podcast television programme]. Sydney: ABC Television. Available:.au/tv/pekingtoparis/podcast/pekingtoparis.xm l. [Accessed Feb. 4, 2008].[2] S. Gary, Presenter, 揃lack Hole Death Ray? StarStuff, Dec. 23, 2007. [Podcast radio programme]. Sydney: ABC News Radio. Available: .au/newsradio/podcast/STARSTUFF.xml. [Accessed Feb. 4, 2008].Other FormatsMicroform[3] W. D. Scott & Co, Information Technology in Australia:Capacities and opportunities: A report to the Department ofScience and Technology. [Microform]. W. D. Scott & CompanyPty. Ltd. in association with Arthur D. Little Inc. Canberra:Department of Science and Technology, 1984.Computer game[4] The Hobbit: The prelude to the Lord of the Rings. [CD-ROM].United Kingdom: Vivendi Universal Games, 2003.Software[5] Thomson ISI, EndNote 7. [CD-ROM]. Berkeley, Ca.: ISIResearchSoft, 2003.Video recording[6] C. Rogers, Writer and Director, Grrls in IT. [Videorecording].Bendigo, Vic. : Video Education Australasia, 1999.A reference list: what should it look like?The reference list should appear at the end of your paper. Begin the list on a new page. The title References should be either left justified or centered on the page. The entries should appear as one numerical sequence in the order that the material is cited in the text of your assignment.Note: The hanging indent for each reference makes the numerical sequence more obvious.[1] A. Rezi and M. Allam, "Techniques in array processing by meansof transformations, " in Control and Dynamic Systems, Vol.69, Multidemsional Systems, C. T. Leondes, Ed. San Diego: Academic Press, 1995, pp. 133-180.[2] G. O. Young, "Synthetic structure of industrial plastics," inPlastics, 2nd ed., vol. 3, J. Peters, Ed. New York:McGraw-Hill, 1964, pp. 15-64.[3] S. M. Hemmington, Soft Science. Saskatoon: University ofSaskatchewan Press, 1997.[4] N. Osifchin and G. Vau, "Power considerations for themodernization of telecommunications in Central and Eastern European and former Soviet Union (CEE/FSU) countries," in Second International Telecommunications Energy SpecialConference, 1997, pp. 9-16.[5] D. Sarunyagate, Ed., Lasers. New York: McGraw-Hill, 1996.[8] O. B. R. Strimpel, "Computer graphics," in McGraw-HillEncyclopedia of Science and Technology, 8th ed., Vol. 4. New York: McGraw-Hill, 1997, pp. 279-283.[9] K. Schwalbe, Information Technology Project Management, 3rd ed.Boston: Course Technology, 2004.[10] M. N. DeMers, Fundamentals of Geographic Information Systems,3rd ed. New York: John Wiley, 2005.[11] L. Vertelney, M. Arent, and H. Lieberman, "Two disciplines insearch of an interface: Reflections on a design problem," in The Art of Human-Computer Interface Design, B. Laurel, Ed.Reading, MA: Addison-Wesley, 1990. Reprinted inHuman-Computer Interaction (ICT 235) Readings and Lecture Notes, Vol. 1. Murdoch: Murdoch University, 2005, pp. 32-37.[12] E. P. Wigner, "Theory of traveling wave optical laser,"Physical Review, vol.134, pp. A635-A646, Dec. 1965.[13] J. U. Duncombe, "Infrared navigation - Part I: An assessmentof feasibility," IEEE Transactions on Electron Devices, vol.ED-11, pp. 34-39, Jan. 1959.[14] M. Bell, et al., Universities Online: A survey of onlineeducation and services in Australia, Occasional Paper Series 02-A. Canberra: Department of Education, Science andTraining, 2002.[15] T. J. van Weert and R. K. Munro, Eds., Informatics and theDigital Society: Social, ethical and cognitive issues: IFIP TC3/WG3.1&3.2 Open Conference on Social, Ethical andCognitive Issues of Informatics and ICT, July 22-26, 2002, Dortmund, Germany. Boston: Kluwer Academic, 2003.[16] I. S. Qamber, "Flow graph development method,"Microelectronics Reliability, vol. 33, no. 9, pp. 1387-1395, Dec. 1993.[17] Australia. Attorney-Generals Department. Digital AgendaReview, 4 Vols. Canberra: Attorney- General's Department, 2003.[18] C. Rogers, Writer and Director, Grrls in IT. [Videorecording].Bendigo, Vic.: Video Education Australasia, 1999.[19] L. Bass, P. Clements, and R. Kazman. Software Architecture inPractice, 2nd ed. Reading, MA: Addison Wesley, 2003. [E-book] Available: Safari e-book.[20] D. Ince, "Acoustic coupler," in A Dictionary of the Internet.Oxford: Oxford University Press, 2001. [Online]. Available: Oxford Reference Online, .[Accessed: May 24, 2005].[21] H. K. Edwards and V. Sridhar, "Analysis of softwarerequirements engineering exercises in a global virtual team setup," Journal of Global Information Management, vol. 13, no. 2, p. 21+, April-June 2005. [Online]. Available: AcademicOneFile, . [Accessed May 31,2005].[22] A. Holub, "Is software engineering an oxymoron?" SoftwareDevelopment Times, p. 28+, March 2005. [Online]. Available: ProQuest, . [Accessed May 23, 2005].[23] H. Zhang, "Delay-insensitive networks," M.S. thesis,University of Waterloo, Waterloo, ON, Canada, 1997.[24] P. H. C. Eilers and J. J. Goeman, "Enhancing scatterplots withsmoothed densities," Bioinformatics, vol. 20, no. 5, pp.623-628, March 2004. [Online]. Available:. [Accessed Sept. 18, 2004].[25] J. Riley, "Call for new look at skilled migrants," TheAustralian, p. 35, May 31, 2005. Available: Factiva,. [Accessed May 31, 2005].[26] European Telecommunications Standards Institute, 揇igitalVideo Broadcasting (DVB): Implementation guidelines for DVB terrestrial services; transmission aspects,?EuropeanTelecommunications Standards Institute, ETSI TR-101-190,1997. [Online]. Available: . [Accessed: Aug. 17, 1998].[27] J. Geralds, "Sega Ends Production of Dreamcast," ,para. 2, Jan. 31, 2001. [Online]. Available:/news/1116995. [Accessed Sept. 12,2004].[28] W. D. Scott & Co, Information Technology in Australia:Capacities and opportunities: A report to the Department of Science and Technology. [Microform]. W. D. Scott & Company Pty. Ltd. in association with Arthur D. Little Inc. Canberra: Department of Science and Technology, 1984.AbbreviationsStandard abbreviations may be used in your citations. A list of appropriate abbreviations can be found below:。

贝叶斯超参数优化是一种用于自动调整机器学习模型超参数的优化技术。

它使用贝叶斯概率理论来估计超参数的最佳值，以优化模型的性能。

多层感知器（MLP）是一种常用的神经网络模型，由多个隐藏层组成，每个层包含多个神经元。

MLP可以用于分类、回归等多种任务。

当使用贝叶斯超参数优化来调整MLP的超参数时，通常会选择一些常见的超参数，如学习率、批量大小、迭代次数等。

贝叶斯优化器会根据这些超参数的性能，选择下一个可能的最佳值。

它通过在每个步骤中随机选择少量的超参数组合，而不是搜索每个可能的组合，来提高效率。

在实践中，贝叶斯超参数优化通常使用一种称为高斯过程回归（Gaussian Process Regression）的方法，该方法可以估计每个超参数的可能值以及它们的概率分布。

然后，根据这些信息选择下一个超参数的值，以最大化模型性能的预期改善。

使用贝叶斯超参数优化可以自动调整超参数，避免了手动调整的困难和耗时。

此外，它还可以帮助找到更好的超参数组合，从而提高模型的性能和准确性。

这对于机器学习任务的实验和开发非常重要，因为它可以帮助快速找到最佳的模型配置。

人工智能导论王万良思考题答案1、下面哪个选项不属于按照形态分类的机器人?（） [单选题] *A.仿人智能机器人B.拟物智能机器人C.对话机器人(正确答案)D.仿生机器人2、下面哪项不属于机器人常用的感觉传感器?（） [单选题] *A.按钮(正确答案)B.视觉C.听觉D.触觉3、下面哪个选项不属于按照使用途径分类的机器人?（） [单选题] *A.工业生产型机器人B.特殊灾害型机器人C.医疗机器人D.行走机器人(正确答案)4、下面哪个选项不属于按照智能程度分类的机器人?（） [单选题] *A.初级智能机器人B.家庭智能陪护机器人C.高级智能机器D.农业机器人(正确答案)5、机器人一般按哪两种方式工作？（） *A.将程序事先写好在存储器中(正确答案)B.示教-再现方式(正确答案)C.手动控制D.自我学习6、下面哪些选项属于机器人常用的传感器？ *A.碰撞传感器(正确答案)B.激光雷达传感器(正确答案)C.视觉传感器(正确答案)D.超声传感器(正确答案)7、麦克风传感器可用于检测语音？ [判断题] *对(正确答案)错8、热释电传感器可用于检测温度？ [判断题] *对错(正确答案)9、碰撞传感器用于检测障碍物时使用？ [判断题] *对(正确答案)错10、激光雷达传感器可用于获取障碍物的精确位置？ [判断题] *对(正确答案)错11、自然语言理解，又称（），是人工智能的一个重要分支，属于计算机科学的一部分 [单选题] *A.人机对话(正确答案)B.人机交互C.语言合成D.语言生成12、下面哪个选项不属于自然语言理解的常用任务？（） [单选题] *A.中文文本分词B.文本表示C.命名实体识别D.文本情感识别(正确答案)13、自然语言处理领域具有两个鲜明特征：一是（），二是真实可用性 [单选题] *A.小规模性B.大规模性(正确答案)C.乱序性D.有序性14、要想提取出“有用”的信息，仅提取关键词、统计词频等是远远不够的，必须对用户数据(尤其是发言、评论等)进行（）。