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

我们已开发multiclass支持vector machines and applied them to diagnosis problems andimage processing. The major results of The project are as follows:1. Development of multiclasssupport vector machines我们已开发的fuzzy support vector machines that resolve unclassifiableregions in multiclass problems. We have developed optimal ordering of decision-tree and pairwisesupport vector machines to improve the generalization ability.2. Development of fast训练methods我们已开发steepest ascent methods for pattern classification and functionapproximation,其中至少有两个数据是在一个时间。3.评估medium to large sized data我们承认我们的方法改善generalization ability and speed up training for4. Application to diagnosis . We have examined the feature extraction based on独立组件分析（ICA） to enhance the discrimination performance of support vectormachines.我们确认ICA could extract the effective features from the gas leakage sound inpipes, digit patterns，and the various benchmark datasets.我们已经开发了evolutionary feature extraction usingmargin maximization method.5. Application to diagnosis, We have developed the multi resolutionfeature extraction method by使用2-dimensional wavelet decomposition for the inputs of supportvector machines.我们已经开发了feature extraction method as a preparation for classifyingthe state of protein crystals by using support vector machines

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.