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

我们已经开发了多尺度支持向量机，并将它们应用于诊断问题和图像处理。该项目的主要结果仍在继续：1.多尺度支持向量机的发展，我们已经开发出了模糊支持向量机，以解决多尺度问题中不可分类的区域。我们已经开发了决策树和配对支持向量机的最佳排序，以提高生成能力。快速培训方法的发展-我们已经为模式分类和功能近似开发了最陡峭的提升方法，在一个时间里处理了两个以上的数据。评估中等大小的数据集·我们确认我们的方法提高了我们的通用性和提高了对大型大小的数据集的培训速度。Application to diagnosis·We have examined the feature extraction based on independent component analysis （ICA） to enhance the discrimination performance of support vector machines。我们确信ICA可以从管道、数字模式和各种基准数据中提取出有效的特征。我们已经开发了使用边际最大化方法的进化特征提取。应用于诊断·我们已经开发了一种多分辨率特征提取方法，使用2-dimensional wavelet decomposition用于支持向量机的输入。我们已经开发了一种特征提取方法，作为一种利用支持向量机分类蛋白质晶体状态的准备方法。

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.