TANC-BIC结构学习算法的改进

基于概率的贝叶斯分类器以其简单的结构和良好的性能受到重视,树扩展朴素贝叶斯分类器TANC应用较广。用TANC-BIC结构学习算法构建的分类器取得了成功,但TANC-BIC结构学习算法未考虑类节点的情况。文中提出了一种新的结构学习TANC-CBIC算法。并在贝叶斯分类器实验平台MBNC上编程实现。实验结果表明,改进算法分类准确率要高于由TANC-BIC和TANC-CMI结构学习算法构建的分类器,TANC-CBIC结构学习算法是有效的。     

基于树型依赖结构的多标记分类算法

在多标记学习中,发现与利用各标记之间的依赖关系能提高学习算法的性能。文中基于分类器链模型提出一种针对性的多标记分类算法。该算法首先量化标记间的依赖程度,并构建标记之间明确的树型依赖结构,从而可减弱分类器链算法中依赖关系的随机性,并将线性依赖关系泛化成树型依赖关系。为充分利用标记间的相互依赖关系,文中采用集成学习技术进一步学习并集成多个不同的标记树型依赖结构。实验结果表明,同分类器链等算法相比,该算法经过集成学习后有更好的分类性能,其能更有效地学习标记间的依赖关系。     

TANC-BIC结构学习算法的改进

基于概率的贝叶斯分类器以其简单的结构和良好的性能受到重视，树扩展朴素贝叶斯分类器TANC应用较广。用TANC—BIC结构学习算法构建的分类器取得了成功，但TANC—BIC结构学习算法未考虑类节点的情况。文中提出了一种新的结构学习TANC—CBIC算法。并在贝叶斯分类器实验平台MBNC上编程实现。实验结果表明，改进算法分类准确率要高于由TANC—BIC和TANC-CMI结构学习算法构建的分类器，TANC—CBIC结构学习算法是有效的。     

一种具有容噪性能的SVM多值分类器

基于 SVM理论的分类器已经发展成为一种通用的二值分类器 .但是它对噪音数据非常敏感 ,而且不适用于多值分类场合 .将标准的 PCA算法扩展到更普遍的领域 ,并提出了一种新的 SVM分类器学习结构 .它使用扩展的 PCA算法对训练集数据进行降噪映射 ,产生一个新的数据集 ,然后通过反对称阵将一组二值分类器组合成一个多值分类器来处理该数据集 .理论分析和试验表明该分类器学习效率高并具有很强的容噪性能     

基于BIC测度和遗传算法的TANC结构学习

树扩展朴素贝叶斯分类器(TANC)是实用性较强的一种分类器,其性能优于朴素贝叶斯分类器。现有的TANC结构学习算法有基于互信息测度的相关性分析方法和贝叶斯信息测度(BIC)的搜索打分方法。将遗传算法引入TANC结构学习,用BIC作为评价函数,提出了基于BIC测度和遗传算法的TANC结构学习算法GA-TANC,并以此构建分类器,用分类准确率衡量算法的性能。实验结果表明,GA-TANC算法有更高的分类准确率,从而说明GA-TANC结构学习算法是准确有效的。     

一种具有容器性能的SVM多值分类器

基于ＳＶＭ理论的分类器已经发展成为一种通用的二值分类器,但是它地噪音数据非常敏感,而且不适用于多值分类场合,将标准的ＰＣＡ算法扩展到更普遍的领域,并提出了一种新的ＳＶＭ分类器学习结构,它使用扩展的ＰＣＡ算法对训练集数据进行降噪声射,产生一个新的数据集,然后通过０反称阵将一组二值分类器组合成一个多值分类器来处理该数据集,理论分析和试验表明该分类器学习效率高并且有很强的容器性能。     

基于改进的在线学习模型的车辆跟踪算法研究

由于道路交叉口环境的复杂性和开放型,对交叉口通行车辆的视频跟踪一直是一个难题。本文提出了一种基于改进的在线学习模型的车辆跟踪算法。该算法通过设计一个级联分类器,包括方差分类器、颜色特征分类器、在线增强学习分类器。实验表明,该算法具有较高的跟踪准确率和实时性。     

一类改进的最小距离分类器的增量学习算法

提出一种基于改进的最小距离分类器的增量学习算法,消除增量学习过程中产生的分类器内部结构的相互干扰,使分类器既能记住已学习的知识,又能学习新知识.增量学习需要对分类器结构进行调整,必须使用有代表性的已学习样本帮助分类器在学习新知识时复习旧知识.针对正态分布的样本集提出一种筛选算法,只保留有代表性的少量样本,大大减少存储消耗和重新训练的计算开销.实验结果证明该算法对样本的识别准确率高,在有效识别新样本的同时对以前学习的样本也保持较高的识别率,消耗存储空间小.     

基于集成学习的Self-training在入侵检测中的应用

针对入侵检测的标记数据难以获得的问题,提出一种基于集成学习的Self-training方法——正则化Self-training。该方法结合主动学习和正则化理论,利用无标记数据对已有的分类器（该分类器对分类模式已学习得很好）作进一步的改进。对三种主要的集成学习方法在不同标记数据比例下进行对比实验,实验结果表明：借助大量无标记数据可以改善组合分类器的分类边界,算法能显著地降低结果分类器的错误率。     

Bagging中文文本分类器的改进方法研究

在文本分类研究中,集成学习是一种提高分类器性能的有效方法.Bagging算法是目前流行的一种集成学习算法.针对Bagging算法弱分类器具有相同权重问题,提出一种改进的Bagging算法.该方法通过对弱分类器分类结果进行可信度计算得到投票权重,应用于Attribute Bagging算法设计了一个中文文本自动分类器.采用kNN作为弱分类器基本模型对Sogou实验室提供的新闻集进行分类.实验表明该算法比Attribute Bagging有更好的分类精度.     

Fuzzy min-max neural networks. I. Classification.

A supervised learning neural network classifier that utilizes fuzzy sets as pattern classes is described. Each fuzzy set is an aggregate (union) of fuzzy set hyperboxes. A fuzzy set hyperbox is an n-dimensional box defined by a min point and a max point with a corresponding membership function. The min-max points are determined using the fuzzy min-max learning algorithm, an expansion-contraction process that can learn nonlinear class boundaries in a single pass through the data and provides the ability to incorporate new and refine existing classes without retraining. The use of a fuzzy set approach to pattern classification inherently provides a degree of membership information that is extremely useful in higher-level decision making. The relationship between fuzzy sets and pattern classification is described. The fuzzy min-max classifier neural network implementation is explained, the learning and recall algorithms are outlined, and several examples of operation demonstrate the strong qualities of this new neural network classifier.     

基于协同进化PSO的分类器学习算法

微粒群算法是一种简单、随机的进化群体算法,能够有效地解决数学性质比较复杂的优化问题。神经网络分类器能够解决复杂的非线性空间上分类的问题,它的训练学习算法要求更简单有效。文中将微粒群优化算法应用于神经网络分类器的学习,并加入协同进化机制以增强其性能。实例表明协同PSO算法的优越性。     

基于神经网络的肺癌细胞识别方法的研究

细胞识别是图像处理和模式识别领域的一个研究热点,有着十分广泛的应用前景。本文提出了基于神经网络算法FTART2的肺癌细胞识别方法,讨论了FTART2的网络结构、输入矢量的标准化及分类算法。用513个样本对网络进行训练,再用716个样本组成测试集进行测试,实验结果表明:本文提出的基于FTART2的肺癌细胞分类器与基于标准BP的分类器相比,具有学习速度快、分类精度高的特点。     

基于表格查寻学习算法的自适应模糊分类器

以手写体数字识别问题为背景,提出了一种基于表格查寻学习算法的自适应模糊分类 器,并用Matlab给出了自适应模糊分类器的实现,进而对其进行了仿真。仿真结果表明,该自适应模 糊分类器在手写体数字识别的识别性能、利用语言信息、计算复杂性等方面均优于采用BP算法的三 层前馈分类器,体现了自适应模糊处理技术用于模式识别的优越性和潜力。     

权值与结构双确定法的RBF神经网络分类器

为了解决径向基函数(RBF)神经网络权值与结构难以确定的问题,基于权值直接确定法,及隐层神经元中心、方差、数目与神经网络性能的关系,提出一种边增边删型的网络权值与结构双确定法。在此方法基础之上,构建一种RBF神经网络分类器并探讨其分类性能和抗噪能力。计算机数值实验结果验证所提出的边增边删型的权值与结构双确定法能够快速、有效地确定网络的中心、方差和网络最优的权值与结构,所构造的模式分类器具有优越的分类性能和抗噪能力。     

Classification of multi‐spectral remote sensing data using a local transfer function classifier

This paper introduces a new neural network, called the local transfer function classifier (LTF‐C), for classification of multi‐spectral remote sensing data. The network structure of LTF‐C is similar to that of the radial basis function neural network (RBF), but LTF‐C utilizes an entirely different learning algorithm. In particular, the network structure of LTF‐C is not predetermined, but changes dynamically during the learning. Such a learning algorithm fits well to the classification problem, and guarantees that the size of the network is as large as is needed. The classification results show that LTF‐C evidently has a better classification accuracy than the six other classifiers in the experiment.     

Polynomial-based radial basis function neural networks (P-RBF NNs) and their application to pattern classification

Polynomial neural networks have been known to exhibit useful properties as classifiers and universal approximators. In this study, we introduce a concept of polynomial-based radial basis function neural networks (P-RBF NNs), present a design methodology and show the use of the networks in classification problems. From the conceptual standpoint, the classifiers of this form can be expressed as a collection of “if-then” rules. The proposed architecture uses two essential development mechanisms. Fuzzy clustering (Fuzzy C-Means, FCM) is aimed at the development of condition parts of the rules while the corresponding conclusions of the rules are formed by some polynomials. A detailed learning algorithm for the P-RBF NNs is developed. The proposed classifier is applied to two-class pattern classification problems. The performance of this classifier is contrasted with the results produced by the “standard” RBF neural networks. In addition, the experimental application covers a comparative analysis including several previous commonly encountered methods such as standard neural networks, SVM, SOM, PCA, LDA, C4.5, and decision trees. The experimental results reveal that the proposed approach comes with a simpler structure of the classifier and better prediction capabilities.     

Extension neural network-type 2 and its applications

A supervised learning pattern classifier, called the extension neural network (ENN), has been described in a recent paper. In this sequel, the unsupervised learning pattern clustering sibling called the extension neural network type 2 (ENN-2) is proposed. This new neural network uses an extension distance (ED) to measure the similarity between data and the cluster center. It does not require an initial guess of the cluster center coordinates, nor of the initial number of clusters. The clustering process is controlled by a distanced parameter and by a novel extension distance. It shows the same capability as human memory systems to keep stability and plasticity characteristics at the same time, and it can produce meaningful weights after learning. Moreover, the structure of the proposed ENN-2 is simpler and the learning time is shorter than traditional neural networks. Experimental results from five different examples, including three benchmark data sets and two practical applications, verify the effectiveness and applicability of the proposed work.     

Multiobjective genetic algorithm partitioning for hierarchicallearning of high-dimensional pattern spaces: alearning-follows-decomposition strategy

We present a novel approach to partitioning pattern spaces using a multiobjective genetic algorithm for identifying (near-)optimal subspaces for hierarchical learning. Our approach of "learning-follows-decomposition" is a generic solution to complex high-dimensional problems where the input space is partitioned prior to the hierarchical neural domain instead of by competitive learning. In this technique, clusters are generated on the basis of fitness of purpose. Results of partitioning pattern spaces are presented. This strategy of preprocessing the data and explicitly optimizing the partitions for subsequent mapping onto a hierarchical classifier is found both to reduce the learning complexity and the classification time with no degradation in overall classification error rate. The classification performance of various algorithms is compared and it is suggested that the neural modules are superior for learning the localized decision surfaces of such partitions and offer better generalization.