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Development of Multiclass Support Vector Machines and Their Application to Diagnosis and Image Processing

多类支持向量机的发展及其在诊断和图像处理中的应用

基本信息

批准号：
14350211
负责人：
ABE Shigeo
金额：
$ 8.26万
依托单位：
Kobe University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2002
资助国家：
日本
起止时间：
2002 至 2003
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-14350211/
关键词：
Support Vector Machine Pattern Recognition Diagnosis Image Processing Multicalss Problem Training Method Fuzzy Inference Kernel Function

项目摘要

We have developed multiclass support vector machines and applied them to diagnosis problems and image processing. The major results of the project are as follows :1. Development of multiclass support vector machines・We have developed fuzzy support vector machines that resolve unclassifiable regions in multiclass problems.・We have developed optimal ordering of decision-tree and pairwise support vector machines to improve the generalization ability.2. Development of fast training methods・We have developed steepest ascent methods for pattern classification and function approximation, in which more than two data are processed at a time.3. Evaluation for medium to large sized data sets・We confirmed that our methods improve the generalization ability and speed up training for large sized data sets.4. Application to diagnosis・We have examined the feature extraction based on independent component analysis (ICA) to enhance the discrimination performance of support vector machines. We confirmed that ICA could extract the effective features from the gas leakage sound in pipes, digit patterns, and the various benchmark datasets.・We have developed the evolutionary feature extraction using margin maximization method.5. Application to diagnosis・We have developed the multi-resolution feature extraction method by using 2-dimensional wavelet decomposition for the inputs of support vector machines.・We have developed the feature extraction method as a preparation for classifying the state of protein crystals by using support vector machines.

我们开发了多类支持向量机，并将其应用于诊断问题和图像处理。本课题的主要研究成果如下：1.多类支持向量机的开发·我们开发了模糊支持向量机，可以解决多类问题中的不可分类区域。我们发展了决策树的最优排序和成对支持向量机来提高泛化能力.快速训练方法的开发·我们开发了用于模式分类和函数逼近的最速上升方法，其中一次处理两个以上的数据。3.中到大规模数据集的评估·我们证实了我们的方法提高了泛化能力，加快了大规模数据集的训练速度。·我们已经研究了基于独立分量分析（伊卡）的特征提取，以提高支持向量机的鉴别性能。我们证实伊卡可以从管道中的气体泄漏声音，数字模式和各种基准数据集中提取有效特征。提出了基于间隔最大化的进化特征提取方法.诊断应用·我们开发了使用二维小波分解作为支持向量机输入的多分辨率特征提取方法。·我们已经开发的特征提取方法作为一个准备，通过使用支持向量机分类的蛋白质晶体的状态。