一种概率映射网络的EM训练算法

本文提出一种概率映射网络（ＰＭＮ）的ＥＭ训练算法。ＰＭＮ为一个四层前馈网。它构成一个贝叶斯分类器,实现多类分类的贝叶斯判别,把输入的样本模式经网络变换为输出的分类判决,其网络节点对应于贝叶斯后验概率公式的各个变量。此ＰＭＮ用高斯核函数作为密度函数,网络参数训练由ＥＭ算法实现,其学习方式为类间的监督学习和类内的非监督学习。最后的实验表明此网络及其学习算法在分类应用中的有效性。     

一种PMN网络的GEM训练算法

本文提出一种概率映射网络的ＧＥＭ训练算法，它是ＥＭ算法的一种改进算法。ＰＭＮ风为一个四层前馈网。它构成一个贝叶斯分类器，实验多类分类的贝叶斯判别，把输入的样本模式经网络变换为输出的分类判决，其网络节点对应于贝叶斯后验概率公式的各个变量。此ＰＭＮ网络用高斯核函数作为密度函数，网络参数的训练由ＧＥＭ算法实现，其学习方式为类间的监督学习和类内的非监督学习。     

一种基于离散属性BNT的双偏振气象雷达降水粒子分类方法

针对传统降水粒子分类算法存在的过度依赖专家经验和模型预设误差问题,本文提出了一种基于离散属性贝叶斯网络(Bayesian NeTwork,BNT)的双偏振气象雷达降水粒子分类(Hydrometeor Classification,HC)方法.首先对双偏振气象雷达获取的偏振参量取值进行离散化处理生成离散化标准,并根据离散化标准制作训练数据集合;然后使用训练数据集合对贝叶斯网络进行结构学习学得贝叶斯网络结构,以及参数学习学得与贝叶斯网络结构匹配的条件概率表;最后加入附加信息计算出每种降水粒子类先验概率,与贝叶斯网络结构和条件概率表共同组成贝叶斯网络分类器.训练好的贝叶斯网络分类器根据最大后验概率准则完成对测试数据的降水粒子分类,与模糊逻辑算法对比评价结果.实验证明:该方法能有效区分不同的降水粒子得到准确的降水粒子分类结果.     

基于贝叶斯信息准则的极化干涉SAR图像非监督分类

该文提出一种利用贝叶斯信息准则自动确定聚类类别数的极化干涉SAR非监督分类算法。该方法首先利用Shannon熵特征对极化干涉SAR图像进行初始分类,然后利用期望最大化(Expectation-Maximization, EM)算法和标号代价(LabelCost)优化算法对分类结果进行迭代优化,同时通过贝叶斯信息准则(Bayesian Information Criterion, BIC)自动确定非监督分类的最佳类别数。实验结果表明该算法能够较准确地确定分类类别数,并具有较为满意的分类效果。     

贝叶斯网络诱导的内积空间

概率模型和核函数相结合的方法是学习系统的热点研究领域,贝叶斯网络是重要的一类概率图形模型。文中主要讨论了变量取值在布尔域上的两类分类任务,重点讨论了几个常见贝叶斯网络诱导的内积空间的最低维数,为解决一些常见的分类问题提供了理论依据。文中通过分析概念类的VC维来确定其欧几里德维数的下界,VC维还可用于估计贝叶斯网络概念类的复杂性和判断概念类的分类性能。     

一种基于遗传算法的数据抽样方法

朴素贝叶斯分类器是一种基于独立假设的贝叶斯定理的简单概率分类器,依靠精确的自然概率模型,在有监督学习的样本集中能获取得非常好的分类效果。本文以朴素贝叶斯分类器为基础,提出一种最优保存简单遗传算法为搜索方法,随机抽样分类测试作为适应性函数来设计实现实例选择算法。实验表明,该抽样方法在不降低朴素贝叶斯分类器精度的前提下明显降低计算代价,对部分数据集还可有效地提高分类器的分类精度。     

基于数据挖掘技术的网络热门舆情分类研究

以提升网络热门舆情分类准确率,降低分类时间为目标,提出了基于数据挖掘技术的网络热门舆情分类方法.将小波核函数和支持向量机结合构成小波模糊支持向量机,采用增量学习机制和贝叶斯分类算法建立增量贝叶斯分类算法,组成小波模糊支持向量机-增量贝叶斯分类算法解决测试样本易分类失误以及类条件独立假定性很难获取问题,通过计算待测样本和...     

类不均衡的半监督高斯过程分类算法

针对传统的监督学习方法难以解决真实数据集标记信息少、训练样本集中存在类不均衡的问题,提出了类不均衡的半监督高斯过程分类算法。算法引入自训练的半监督学习思想,结合高斯过程分类算法计算后验概率,向未标记数据中注入类标记以获得更多准确可信的标记数据,使得训练样本的类分布相对平衡,分类器自适应优化以获得较好的分类效果。实验结果表明,在类不均衡的训练样本及标记信息过少的情况下,该算法通过自训练分类器获得了有效标记,使分类精度得到了有效提高,为解决类不均衡数据分类提供了一个新的思路。     

监督学习在无线认知传感器网络中的应用

监督学习是指利用一组已知类型的训练数据(输入对象和预期输出)对学习系统进行训练,调整分类器的参数,预测新输入的输出值。机器学习可用于无线认知传感器网络中的学习引擎设计和MAC(媒体访问控制)协议分类。文章介绍线性回归、Logistic回归、人工神经网络、决策树学习、随机森林、朴素贝叶斯分类器、支持向量机等常用监督学习算法的原理,并简要分析机器学习在无线认知传感器网络中的应用场景。     

基于半监督生成对抗网络的乳腺癌图像分类

本文针对仅有少量带标签样本时如何提高大量未标 注样本分类的的鲁棒性和准确性问题,提出一种 基于改进的半监督生成对抗网络(semi-supvised generative adversarial networks,SGAN) 的乳腺癌图像分类方法。该方法在输出层使用Softmax 函数 替代 Sigmoid 函数实现多分类。首先将随机向量输入到生成网络中,生成伪样本并标记为伪样本 类进行训 练。接着将真实标签样本、真实无标签样本和伪样本输入到判别网络中,输出为不同类概率 值;然后采 用半监督训练方法反向传播更新参数;最后实现对乳腺癌病理图像的分类,标注样本数量分 别为25、 50和200,最终准 确率达到95.5%。实验结果表明,当标注 样本有限时,本文算法的准确 率具有良好 的鲁棒性。本文算法相比于使用卷积神经网络和迁移学习(tranfer learning,TL)等分类方法准确率有了显著提高。     

基于GMM和神经网络的辐射源识别方法

针对基于截获雷达脉冲特征参数的辐射源识别问题,通过建立一个高斯混合模型(GMM),采用最大化期望(EM)方法对模型参数进行训练,构建了一个输入为截获雷达脉冲特征参数,输出为雷达辐射源类型的分类器。同时,为实现对分类识别性能对比,进一步提出基于神经网络方法构建雷达辐射源类型分类器。仿真试验结果表明,基于GMM和神经网络构建的两种分类器均能实现对雷达辐射源的在线识别,且当用于训练的样本比例不低于10%时,均能获得90%以上的分类正确率。     

基于高斯混合模型的车辆字符识别算法

在基于统计方法的车牌字符识别算法中,为了更有效地描述车牌字符特征的类条件概率密度,提出了一种基于高斯混合模型(GMM)的识别方法.首先采用Gabor变换来提取车牌字符的特征参数,然后通过GMM来逼近字符特征的类条件概率密度分布,最后根据得到的类条件概率密度分布函数构造贝叶斯分类器.其中,GMM的参数估计采用EM算法.实验表明,这种方法具有良好的字符识别效果、较强的鲁棒性以及较大的应用价值.     

一种新的高斯基函数分类器训练方法的研究

本文主要研究高斯基函数分类器的训练问题，对基函数分类器来说，如何确定网络的初始隐层节点数和隐层节点参数是一个重要问题，文中采用基于遗传算法的高斯核函数聚类方法和模糊决策技术完成分类器的初始结构和参数确定，然后再采用反向传播（ＢＰ）学习算法完成分类器的最终训练，典型实验的结果表明了该方法的有效性。     

A Gaussian adaptive resonance theory neural network classificationalgorithm applied to supervised land cover mapping using multitemporalvegetation index data

Neural network classifiers have been shown to provide supervised classification results that significantly improve on traditional classification algorithms such as the Bayesian (maximum likelihood [ML]) classifier. While the predominant neural network architecture has been the feedforward multilayer perceptron known as backpropagation. Adaptive resonance theory (ART) neural networks offer advantages to the classification of optical remote sensing data for vegetation and land cover mapping. A significant advantage is that it does not require prior specification of the neural net structure, creating as many internal nodes as are needed to represent the calibration (training) data. The Gaussian ARTMAP classification algorithm bases the probability that input training samples belong to specific classes on the parameters of its Gaussian distributions: the means, standard deviations, and a priori probabilities. The performance of the Gaussian ARTMAP classification algorithm in terms of classification accuracy using independent validation data indicated was over 70% accurate when applied to an annual series of monthly 1-km advanced very high resolution radiometer (AVHRR) satellite normalized difference vegetation index (NDVI) data. The accuracies were comparable to those of fuzzy ARTMAP and a univariate decision tree, and significantly higher than a Bayesian classification algorithm. Algorithm testing is based on calibration and validation data developed and applied to Central America to map the International Geosphere-Biosphere Programme (IGBP) land cover classification system     

Training Classifiers for Tree-structured Categories with Partially Labeled Data

In this paper we propose a new method for training classifiers for multi-class problems when classes are not (necessarily) mutually exclusive and may be related by means of a probabilistic tree structure. It is based on the definition of a Bayesian model relating network parameters, feature vectors and categories. Learning is stated as a maximum likelihood estimation problem of the classifier parameters. The proposed algorithm is specially suited to situations where each training sample is labeled with respect to only one or part of the categories in the tree. Our experiments on information retrieval scenarios show the advantages of the proposed method.     

Distributed EM algorithms for density estimation and clustering in sensor networks

The paper considers the problem of density estimation and clustering in distributed sensor networks. It is assumed that each node in the network senses an environment that can be described as a mixture of some elementary conditions. The measurements are thus statistically modeled with a mixture of Gaussians, where each Gaussian component corresponds to one of the elementary conditions. The paper presents a distributed expectation-maximization (EM) algorithm for estimating the Gaussian components, which are common to the environment and sensor network as a whole, as well as the mixing probabilities that may vary from node to node. The algorithm produces an estimate (in terms of a Gaussian mixture approximation) of the density of the sensor data without requiring the data to be transmitted to and processed at a central location. Alternatively, the algorithm can be viewed as a distributed processing strategy for clustering the sensor data into components corresponding to predominant environmental features sensed by the network. The convergence of the distributed EM algorithm is investigated, and simulations demonstrate the potential of this approach to sensor network data analysis.     

基于自适应EM算法的光学图像海域分割

提出了一种针对光学遥感图像海域分割的自适应期望最大算法(Expectation Maximization,EM)。传统EM算法需要给出高斯混合模型的个数,文中所做的改进使其具有自适应确定高斯模型个数的能力,由此可以降低由于个数选择的不合理带来的错误分割的风险。将该方法应用低分辨率复杂背景遥感图像的海域分割,取得了较理想结果。     

EM-based blind LDPC identification in multipath channels

As the advent of cognitive radios,blind encoder identification has attracted increasingly attentions since it plays an important role.The existing works mainly focus on additive white Gaussian noise (AWGN) channel,while the blind identification in multipath scenarios has not been sufficiently investigated.Considering the blind low density parity-check (LDPC) codes identification in the presence of unknown multipath fading channel,a likelihood-based classifier was proposed using the expectation maximization (EM) algorithm to obtain the maximum likelihood estimates of the unknown parameters.Then,an average log-likelihood ratio (LLR) estimator was adopted to classify the unknown encoder.Numerical results show that the proposed algorithm provides promising identification performance in multipath channels,especially in the low signal-to-noise ratio region.     

基于负相关神经网络集成的货币识别算法研究

为了提高货币识别率,提出了用负相关学习算法来提高神经网络集成的泛化能力.将紫外光照射下的纸币图片作为实验样本,将负相关学习法的集成神经网络用于分类器设计,选择6种面额纸币在不同噪声下的样本共300个作为训练样本,对单个神经网络分类器和神经网络集成分类器进行了MATLAB仿真,并对仿真所得的可靠性、识别率进行对比.实验结果表明,基于负相关学习的神经网络集成对货币识别分类有很好的效果,与应用单个神经网络的系统和独立训练个体网络的集成神经网络相比,它的识别率平均可以高出4％.