基于W^1 2再生核支持向量机的模式分类研究

支持向量机是基于统计学习理论的模式分类器。它通过结构风险最小化准则和核函数方法，较好地解决了模式分类器复杂性和推广性之间的矛盾，引起了大家对模式识别领域的极大关注。近年来，支持向量机在手写体识别、人脸识别、文本分类等领域取得了很大的成功。文章将一种新的核函数用于虹膜识别，并与传统的多项式核函数、高斯核函数进行了比较。初步结果显示了该核函数的应用潜力。     

基于W12再生核支持向量机的模式分类研究

支持向量机是基于统计学习理论的模式分类器.它通过结构风险最小化准则和核函数方法,较好地解决了模式分类器复杂性和推广性之间的矛盾,引起了大家对模式识别领域的极大关注.近年来,支持向量机在手写体识别、人脸识别、文本分类等领域取得了很大的成功.文章将一种新的核函数用于虹膜识别,并与传统的多项式核函数、高斯核函数进行了比较.初步结果显示了该核函数的应用潜力.     

基于W_2~1再生核支持向量机的模式分类研究

支持向量机是基于统计学习理论的模式分类器。它通过结构风险最小化准则和核函数方法,较好地解决了模式分类器复杂性和推广性之间的矛盾,引起了大家对模式识别领域的极大关注。近年来,支持向量机在手写体识别、人脸识别、文本分类等领域取得了很大的成功。文章将一种新的核函数用于虹膜识别,并与传统的多项式核函数、高斯核函数进行了比较。初步结果显示了该核函数的应用潜力。     

融合知识图谱与Bert+CNN的图书文本分类研究

基于海量图书的分类需求，提出了一种融合知识图谱（Knowledge Graph, KG）与转换器双向编码器（Bidirectional Encoder Representation from Transformers, Bert）的图书文本分类模型。通过构建面向图书领域知识图谱扩展图书文本的语义信息，并且使用深度学习的方法获取文本深层语义信息，将扩展语义信息与深层语义信息相结合后，通过TextCNN进行图书分类。经研究试验表明，融合知识图谱与深度学习的图书文本分类算法相对于只使用深度学习进行图书分类算法，前者的分类效果更佳。     

基于多核融合的中文领域实体关系抽取

针对传统径向基核函数的训练矩阵中所有元素都十分接近零而不利于分类的问题,该文提出了一种融合了改进的径向基核函数及其他核函数的多核融合中文领域实体关系抽取方法。利用径向基核函数的数学特性,提出一种改进的训练矩阵,使训练矩阵中的向量离散化,并以此改进的径向基核函数融合多项式核函数及卷积树核函数,通过枚举的方式寻找最优的复合核函数参数,并以上述多核融合方法与支持向量机结合进行中文领域实体关系抽取。在旅游领域的语料上测试,相对于单一核方法及传统多核融合方法,关系抽取性能得到提高。     

融合不变性特征的简易支持向量机

应用样本数据具有不变性特征的特点，提出基于虚拟向量变换和局部相关性特征的简易SVM(支持向量机)方法，并以USPS手写体数字识别为例子，与切距核、虚拟SVM、人工识别等方法进行对比，发现该方法能获得较好的粗识别率且计算时间最少。结果表明，该方法能充分提取样本数据中的不变性特征，是研究模式分类问题的有效方法。     

运用文本领域的常识改善基于支撑向量机的文本分类器性能

本文提出了一种提高中文文本分类器推广性能的方法。一般而言,采用机器学习的方法对文本集合进行训练,可以获得文本分类器。本文引入了文本语义不变性常识,并将其融合到文本分类器中,提出了改进文本分类器的方法。与支撑向量机相结合,设计并实现了改进的文本分类器。对中文文本分类的实验表明,文本语义不变性常识的运用有效地改善了分类器的推广性能。     

一种有效的手写体汉字组合特征的抽取与识别算法

基于特征融合的思想,从有利于模式分类的角度,推广了典型相关分析的理论,建立了广义的典型相关分析用于图像识别的理论框架。在该框架下,首先利用广义的典型相关判据准则函数,求取两组特征矢量的广义投影矢量集,构成一对变换矩阵;然后根据所提出的新的特征融合策略,对两种手写体汉字特征进行融合,所抽取的模式的相关特征矩阵,在普通分类器下取得了良好的分类效果,优于已有的特征融合方法及基于单一特征的PCA 方法和FLDA 方法。     

Walsh变换核矩阵的简单生成及其应用

本文讨论了Walsh函数的Walsh序、Paley序与Hadamard序相应的变换核矩阵的相互转化关系，给出了三类序的Walsh变换核矩阵的生成算法，且生成算法简单，还给出了Matlab生成该类矩阵的Matlab程序，并将几类矩阵的转换置换矩阵应用到图像信息的加密置乱中，置乱效果很好。     

融合标签语义知识的价值观多标签文本分类

为了准确识别网络文本中的价值观倾向,该文提出了一种融合标签语义知识实现价值观多标签文本分类的策略。首先基于价值观理论体系,构建了价值观知识图谱;然后构建了价值观多标签文本分类数据集;最后提出了融合标签语义知识的价值观多标签文本分类模型,通过两种方式融合价值观标签的语义知识。其一,利用标签语义信息进行文本表示学习,获得每个标签对于文本中不同词的重要程度;其二,利用标签的语义知识,计算标签与文本的语义相似度,并与分类模型结果融合。实验表明,该方法可以较好地解决价值观多标签分类问题,尤其可以缓解“尾标签”问题,最终在top@1结果上达到62.44%的精确率,在top@3上达到66.92%的召回率。     

基于免疫遗传算法的支持向量机参数优化及其应用

支持向量机是在统计学习理论基础上发展起来的一种性能优良的新型机器学习方法,它具有坚实的理论基础,巧妙的算法实现。支持向量机的卓越性能依赖于它的参数的正确选择。本文采用改进的免疫遗传算法对支持向量机的参数进行优化。实验证明对于低维数据分类时,本文的优化算法比传统的网格法可以较大减少参数优化时间和提升分类的准确率。对高维的文本数据分类时,在保证分类准确率的前提下,仍然可以较大减少优化的时间。     

Image category learning and classification via optimal linear combination of multiple partially matching kernels

Multiple kernel learning (MKL) aims at simultaneously optimizing kernel weights while training the support vector machine (SVM) to get satisfactory classification or regression results. Recent publications and developments based on SVM have shown that by using MKL one can enhance interpretability of the decision function and improve classifier performance, which motivates researchers to explore the use of homogeneous model obtained as linear combination of various types of kernels. In this paper, we show that MKL problems can be solved efficiently by modified projection gradient method and applied for image categorization and object detection. The kernel is defined as a linear combination of feature histogram function that can measure the degree of similarity of partial correspondence between feature sets for discriminative classification, which allows recognition robust to within-class variation, pose changes, and articulation. We evaluate our proposed framework on the ETH-80 dataset for several multi-level image encodings for supervised and unsupervised object recognition and report competitive results.     

基于连续小波和支持向量机分类音乐类型

音乐类型分类主要包括两个阶段：特征提取和分类。文中在研究小波变换理论基础上,采用连续小波分析方法提取音乐特征参数。支持向量机是专门针对有限样本情况下的一种分类方法。它是建立在统计学习理论的VC维理论和结构风险最小原理基础上,根据有限的样本信息在模型的复杂性和学习能力之间寻求最佳折衷,以期获得最好的推广能力。采用指数径向基函数（脚）内核,分类正确率可达85％,比传统的混合高斯模型和K近邻分类器,分类性能分别提高了21％和23％。实验结果表明,采用小波和支持向量机方法是一种相当有效的音乐类型分类方法。     

Design of semi-tensor product-based kernel function for SVM nonlinear classification

The kernel function method in support vector machine (SVM) is an excellent tool for nonlinear classification. How to design a kernel function is difficult for an SVM nonlinear classification problem, even for the polynomial kernel function. In this paper, we propose a new kind of polynomial kernel functions, called semi-tensor product kernel (STP-kernel), for an SVM nonlinear classification problem by semi-tensor product of matrix (STP) theory. We have shown the existence of the STP-kernel function and verified that it is just a polynomial kernel. In addition, we have shown the existence of the reproducing kernel Hilbert space (RKHS) associated with the STP-kernel function. Compared to the existing methods, it is much easier to construct the nonlinear feature mapping for an SVM nonlinear classification problem via an STP operator.     

一种新的李群分类器在手写体数字中的应用

李群是变换空间的一种基本表示理论。目前针对李群数据所设计的分类器较少,对多分类的效果也不是很好。以手写体数字的应用为背景,引入了支持向量机分类算法来处理李群数据。由于李群数据具有矩阵表现的形式,设计了一种矩阵高斯核函数,使得支持向量机能够处理矩阵数据。仿真结果表明,支持向量机方法在李群数据上具有很好的性能。     

瓷砖图像的纹理特征分类研究

在已有的瓷砖图像分类系统中,仅靠颜色特征和简单的纹理边缘信息只能对无花纹的单色砖或简单花纹的瓷砖进行有效分类,对复杂图案的瓷砖存在识别率低的问题。针对此种情况,结合瓷砖图像的灰度共生矩阵和统计几何特征,将这些特征输入支持向量机进行特征分层分类。采用基于径向基核函数和[K]交叉验证法所得到的最优参数构造支持向量机,解决瓷砖纹理特征具有非线性的分类问题。用瓷砖生产线上采集的大量图像进行实验表明,该方法准确率高,分类效果好。     

The kernel common vector method: a novel nonlinear subspace classifier for pattern recognition.

The common vector (CV) method is a linear subspace classifier method which allows one to discriminate between classes of data sets, such as those arising in image and word recognition. This method utilizes subspaces that represent classes during classification. Each subspace is modeled such that common features of all samples in the corresponding class are extracted. To accomplish this goal, the method eliminates features that are in the direction of the eigenvectors corresponding to the nonzero eigenvalues of the covariance matrix of each class. In this paper, we introduce a variation of the CV method, which will be referred to as the modified CV (MCV) method. Then, a novel approach is proposed to apply the MCV method in a nonlinearly mapped higher dimensional feature space. In this approach, all samples are mapped into a higher dimensional feature space using a kernel mapping function, and then, the MCV method is applied in the mapped space. Under certain conditions, each class gives rise to a unique CV, and the method guarantees a 100% recognition rate with respect to the training set data. Moreover, experiments with several test cases also show that the generalization performance of the proposed kernel method is comparable to the generalization performances of other linear subspace classifier methods as well as the kernel-based nonlinear subspace method. While both the MCV method and its kernel counterpart did not outperform the support vector machine (SVM) classifier in most of the reported experiments, the application of our proposed methods is simpler than that of the multiclass SVM classifier. In addition, it is not necessary to adjust any parameters in our approach.     

SVM在多源遥感图像分类中的应用研究

在利用遥感图像进行土地利用/覆盖分类过程中,可采用以下两种途径来提高分类精度:一是通过增加有利于分类的数据源,引入地理辅助数据和归一化植被指数(NDVI)来进行多源信息融合;二是选择更好的分类方法,例如支持向量机(SVM)学习方法,由于该方法克服了最大似然法和神经网络的弱点,非常适合高维、复杂的小样本多源数据的分类。为了提高多源遥感图像分类的精度,还研究了支持向量机在遥感图像分类中模型的选择,包括多类模型和核函数的选择。分类结果表明,支持向量机比传统的分类方法具有更高的精度,尤其是基于径向基核函数和一对一多类方法的支持向量机模型更适合多源遥感图像分类,因此,基于支持向量机的多源土地利用/覆盖分类能大大提高分类精度。     

Support vector learning for fuzzy rule-based classification systems

To design a fuzzy rule-based classification system (fuzzy classifier) with good generalization ability in a high dimensional feature space has been an active research topic for a long time. As a powerful machine learning approach for pattern recognition problems, the support vector machine (SVM) is known to have good generalization ability. More importantly, an SVM can work very well on a high- (or even infinite) dimensional feature space. This paper investigates the connection between fuzzy classifiers and kernel machines, establishes a link between fuzzy rules and kernels, and proposes a learning algorithm for fuzzy classifiers. We first show that a fuzzy classifier implicitly defines a translation invariant kernel under the assumption that all membership functions associated with the same input variable are generated from location transformation of a reference function. Fuzzy inference on the IF-part of a fuzzy rule can be viewed as evaluating the kernel function. The kernel function is then proven to be a Mercer kernel if the reference functions meet a certain spectral requirement. The corresponding fuzzy classifier is named positive definite fuzzy classifier (PDFC). A PDFC can be built from the given training samples based on a support vector learning approach with the IF-part fuzzy rules given by the support vectors. Since the learning process minimizes an upper bound on the expected risk (expected prediction error) instead of the empirical risk (training error), the resulting PDFC usually has good generalization. Moreover, because of the sparsity properties of the SVMs, the number of fuzzy rules is irrelevant to the dimension of input space. In this sense, we avoid the "curse of dimensionality." Finally, PDFCs with different reference functions are constructed using the support vector learning approach. The performance of the PDFCs is illustrated by extensive experimental results. Comparisons with other methods are also provided.