当前位置：文档之家 › A Fuzzy Bayes Evaluator for On-Line Training Evaluation Based on Virtual Reality

A Fuzzy Bayes Evaluator for On-Line Training Evaluation Based on Virtual Reality

  • 格式：pdf
  • 大小：399.65 KB
  • 文档页数：5

VIRTUAL REALITY AND SIMULATED TRAINING
Virtual Reality refers to real-time systems modeled by computer graphics that allow user interaction and movements with three or more degrees of freedom [2, 27]. More than a technology, virtual reality became a new science that joins several fields as computers, robotics, graphics, engineering and cognition. Virtual Reality Worlds are 3D environments created by computer graphics techniques where one or more users are immersed totally or partially to interact with virtual elements. The quality of the user experience in a virtual reality world is given by the graphics resolution and by the use of special devices for interaction. Basically, the devices stimulate the human senses as vision, audition and touch: head-mounted displays (HMD) or even conventional monitors combined with special glasses can provide stereoscopic visualization; multiple sound sources positioned provides 3D sound; and touch can be simulated by the use of haptic devices [11, 13]. Virtual reality systems for training provide significant benefits over other methods of training, mainly in critical medical procedures. In some cases, those procedures are
1 Ronei Marcos de Moraes, Department of Statistics, Universidade Federal da Paraíba, Cidade Universitária s/n CEP 58.051-900 João Pessoa – PB - Brazil, phone.: +55 83 3216-7075, ronei@de.ufpb.br 2 Liliane dos Santos Machado, Department of Computer Sciences, Universidade Federal da Paraíba, Cidade Universitária s/n CEP 58.051-900 João Pessoa – PB - Brazil, liliane@di.ufpb.br.
A FUZZY BAYES EVALUATOR FOR ON-LINE TRAINING EVALUATION BASED ON VIRTUAL REALITY
Ronei Marcos de Moraes 1 and Liliane dosstract Virtual reality systems for training provide significant benefits over other methods, mainly in critical procedures. The evaluation of simulations is necessary to assess the training quality and provide some feedback about the user performance. An on-line evaluator for training in simulators based on virtual reality must have a low complexity algorithm to do not compromise the performance of the simulator. Several approaches to perform on-line or off-line evaluation for training simulators based on virtual reality have been proposed. We propose a new approach to on-line evaluation using a Fuzzy Bayes evaluator for modeling and classification of simulation in pre-defined classes of training. This method allows the use of continuous variables without lost of information. So, it solves the problem of low complexity in on-line evaluators without compromise performance of the simulator. Index Terms Online Training Evaluation, Fuzzy Bayes Evaluator, Virtual Reality. cannot be simply classified as bad or good due to its complexity. So, the existence of an on-line evaluation tool attached to a simulation system based on virtual reality is important to allow the learning improvement and users evaluation. Recently, new methods of evaluation for on-line training in virtual reality simulator have been proposed [4, 6, 7, 8, 9, 10, 14, 16, 17, 18, 19, 20, 21, 23, 24]. In medicine, some models for off-line or on-line evaluation of training have been proposed too. However, great part of these approaches depends on a large computational structure, which is very expensive to be available in some Medical Centers in Brazil and several other countries. Simulators based on virtual reality (VR) for training need high-end computers to provide realistic haptics, stereoscopic visualization of 3D models, interactive deformation and textures. On-line evaluators must have low complexity to do not compromise performance of the simulations, but they must have high accuracy to do not compromise the evaluation. A method based on Fuzzy Bayes Rule can be a good option for an on-line training evaluator in virtual reality simulators.
INTRODUCTION
With the technological progress several kinds of training are already executed in virtual reality simulators [2]. There are many purposes for virtual reality systems, but a very important one is the simulation of procedures for training. Virtual reality systems for training provide significant benefits over other methods, mainly in critical procedures. One example of training based on VR systems is the flight simulators used for the pilots' training in the civil aviation [25]. In medicine, the use of virtual reality systems for training is beneficial in cases where a mistake could result in physical or emotional impact on patients. Systems for different modalities in medicine have been developed, as training in laparoscopy [28], prostate examination [1], ocular surgery [12] and bone marrow harvest [5]. In several kinds of training based on virtual reality, the user performance is recorded in videotapes and post analyzed by experts. After some time, the user receives his evaluation. This is a problem because probably after some hours the user will not remember his exact actions when was performing the simulation. This situation will make difficult the use of the evaluation information to improve his performance. Besides of that, in several cases a training

合集下载

On Fuzzy Internal Rate of Return

On Fuzzy Internal Rate of Return


and we use the notation A = (a, α). If α = 0 then A collapses to the characteristic function of {a} ⊂ I R and we write A = a ¯. A triangular fuzzy number with center a may be seen as a fuzzy quantity ”x is approximately equal to a”.
Figure 1: Graph of a 2-year project a = (−1, 1, 1) with IRR = 61.8%. Then NPV is a strictly monotone decreasing function of r and the equation (1) has a unique solution, moreover, the discount rate r can be interpreted in strictly financial terms as an interest rate. Now in an accept-or-reject decision it is clear that, if the market rate of interest is r0 , the project should be accepted if r∗∗ > r0 because this implies the that NPV at r0 is positive. In comparing two projects, the one with the higher IRR should be preferred. 1
1 a-α a a+α
A GIRD-BASED FUZZY CLUSTER APPROACH

A GIRD-BASED FUZZY CLUSTER APPROACH


Keywords:
Grid-based Partition; Clustering Algorithm; Fuzzy Clustering; Initialization
1.
Introduction
Fuzzy clustering has been used very successfully in many applications. A few mathematical formalisms are available to perform a measure of the uncertainty and the inaccuracy. Among these, the fuzzy clustering is a a powerful and flexible mathematical tool for handling uncertain, imprecise, and incomplete information. There are at least three reasons for this. First, representing the uncertainty and the inaccuracy of a body of knowledge via the notion of fuzzy membership degree can be committed to one single hypothesis (singleton) or a composite hypothesis (union of hypotheses). Second, the fuzziness exponent can represent the uncertainty in a flexible and efficient manner. Finally, the fuzzy clustering directly leads to many excellent applications such as information granular [1-3], fuzzy control rule [4-6] and etc. FCM (Fuzzy C-Means) is the most widely used fuzzy clustering approach. Despite success, FCM suffers largely from the following threshold of "interesting area " (e.g., the size of cluster)
基于图正则化的贝叶斯宽度学习系统

基于图正则化的贝叶斯宽度学习系统

第4卷第1期智能科学与技术学报V ol.4No.1 2022年3月Chinese Journal of Intelligent Science and Technology March 2022 基于图正则化的贝叶斯宽度学习系统段俊伟1，许林灿1，全渝娟1，陈龙2，陈俊龙3（1. 暨南大学信息科学技术学院，广东广州 510632；2. 澳门大学科技学院，澳门 999078；3. 华南理工大学计算机科学与工程学院，广东广州 510006）摘 要：作为一种前馈神经网络，宽度学习系统因其精度高、训练速度快且能有效代替深度学习方法而备受研究者的关注。

然而，宽度学习系统存在对网络中的特征节点个数比较敏感且求伪逆方式易使模型出现过拟合等问题。

为此，在宽度学习系统中引入贝叶斯推断和图正则化。

一方面，通过引入先验知识进行贝叶斯学习可以有效提高权重的稀疏性，提高模型的稳定性；另一方面，加入图正则化可充分考虑数据内在的图信息，进一步提高模型的泛化能力。

在UCI数据集和NORB数据集上对所提模型进行性能评估，实验结果表明，所提的基于图正则化的贝叶斯宽度学习系统模型能进一步提高宽度学习系统的分类精度且具有更好的稳定性。

关键词：宽度学习系统；贝叶斯推断；图正则化；模式识别中图分类号：TP181文献标志码：Adoi: 10.11959/j.issn.2096−6652.202203Graph-regularized Bayesian broad learning systemDUAN Junwei1, XU Lincan1, QUAN Yujuan1, CHEN Long2, CHEN C.L. Philip 31. College of Information Science and Technology, Jinan University, Guangzhou 510632, China2. Faculty of Science and Technology, University of Macau, Macau 999078, China3. School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, ChinaAbstract: As a feed forward neural network, broad learning system (BLS) has attracted much attention because of its high accuracy, fast training speed, and the ability to effectively replace deep learning methods. However, it is sensitive to the number of feature nodes and the pseudo-inverse method is likely to result in the problem of over fitting for BLS mod-el. To address the above issues, Bayesian inference and graph regularization was introduced in to the BLS model. By in-troducing the prior knowledge for Bayesian learning, the sparsity of the weights and the stability of the model could be effectively improved; while the graph information mining from the data could be fully considered to improve the genera-lization ability of the model by regularization. The UCI and NORB dataset were adopted for evaluating the performance of the proposed model. The experiment results demonstrated that the proposed graph-regularized Bayesian broad learning system model can further improve the accuracy of classification and has better stability.Key words: board learning system, Bayesian inference, graph regularization, pattern recognition收稿日期：2021−02−01；修回日期：2021−04−17通信作者：全渝娟，Tquanyj@基金项目：国家重点研发计划基金资助项目（No.2018YFC2002500）；广东省基础与应用基础研究基金资助项目（No. 2021A1515011999）；广州市科技创新发展专项资金项目（No.201902010041）Foundation Items: The National Key Research and Development Program of China (No.2018YFC2002500), The Guangdong Basic and Applied Basic Research Foundation (No.2021A1515011999), The Guangzhou Science and Technology Innovation and Devel-opment Special Fund Project (No.201902010041)·110·智能科学与技术学报第3卷0引言目前，利用人工智能技术快速准确地获取数据并进行分析与处理已成为被广泛关注的问题之一[1-2]。

高斯朴素贝叶斯训练集精确度的英语

高斯朴素贝叶斯训练集精确度的英语

高斯朴素贝叶斯训练集精确度的英语Gaussian Naive Bayes (GNB) is a popular machine learning algorithm used for classification tasks. It is particularly well-suited for text classification, spam filtering, and recommendation systems. However, like any other machine learning algorithm, GNB's performance heavily relies on the quality of the training data. In this essay, we will delve into the factors that affect the training set accuracy of Gaussian Naive Bayes and explore potential solutions to improve its performance.One of the key factors that influence the training set accuracy of GNB is the quality and quantity of the training data. In order for the algorithm to make accurate predictions, it needs to be trained on a diverse and representative dataset. If the training set is too small or biased, the model may not generalize well to new, unseen data. This can result in low training set accuracy and poor performance in real-world applications. Therefore, it is crucial to ensure that the training data is comprehensive and well-balanced across different classes.Another factor that can impact the training set accuracy of GNB is the presence of irrelevant or noisy features in the dataset. When the input features contain irrelevant information or noise, it can hinder the algorithm's ability to identify meaningful patterns and make accurate predictions. To address this issue, feature selection and feature engineering techniques can be employed to filter out irrelevant features and enhance the discriminative power of the model. Byselecting the most informative features and transforming them appropriately, we can improve the training set accuracy of GNB.Furthermore, the assumption of feature independence in Gaussian Naive Bayes can also affect its training set accuracy. Although the 'naive' assumption of feature independence simplifies the model and makes it computationally efficient, it may not hold true in real-world datasets where features are often correlated. When features are not independent, it can lead to biased probability estimates and suboptimal performance. To mitigate this issue, techniques such as feature extraction and dimensionality reduction can be employed to decorrelate the input features and improve the training set accuracy of GNB.In addition to the aforementioned factors, the choice of hyperparameters and model tuning can also impact the training set accuracy of GNB. Hyperparameters such as the smoothing parameter (alpha) and the covariance type in the Gaussian distribution can significantly influence the model's performance. Therefore, it is important to carefully tune these hyperparameters through cross-validation andgrid search to optimize the training set accuracy of GNB. By selecting the appropriate hyperparameters, we can ensure that the model is well-calibrated and achieves high accuracy on the training set.Despite the challenges and limitations associated with GNB, there are several strategies that can be employed to improve its training set accuracy. By curating a high-quality training dataset, performing feature selection and engineering, addressing feature independence assumptions, and tuning model hyperparameters, we can enhance the performance of GNB and achieve higher training set accuracy. Furthermore, it is important to continuously evaluate and validate the model on unseen data to ensure that it generalizes well and performs robustly in real-world scenarios. By addressing these factors and adopting best practices in model training and evaluation, we can maximize the training set accuracy of Gaussian Naive Bayes and unleash its full potential in various applications.。