基于活体肝细胞的31P磁共振波谱图(31Phosphorus Magnetic Resonance Spectroscopy,31P-MRS)对肝细胞数据进行诊断,分为3种类型:肝癌、肝硬化和正常肝。分别运用线性分类器和二次分类器对数据分类,并在分类前进行了特征抽取。线性分类...基于活体肝细胞的31P磁共振波谱图(31Phosphorus Magnetic Resonance Spectroscopy,31P-MRS)对肝细胞数据进行诊断,分为3种类型:肝癌、肝硬化和正常肝。分别运用线性分类器和二次分类器对数据分类,并在分类前进行了特征抽取。线性分类器和二次分类器在"留一法"中对上述3种类型的分类准确率分别约为81.37%、77.75%、92.30%和95.27%、99.89%、99.70%。实验证明二次分类器相对于线性分类器,明显地提高了分类准确率。展开更多
Currently there are two approaches for a multi-class support vector classifier(SVC). One is to construct and combine several binary classifiers while the other is to directly consider all classes of data in one optimi...Currently there are two approaches for a multi-class support vector classifier(SVC). One is to construct and combine several binary classifiers while the other is to directly consider all classes of data in one optimization formulation. For a K-class problem(K>2),the first approach has to construct at least K classifiers,and the second approach has to solve a much larger op-timization problem proportional to K by the algorithms developed so far. In this paper,following the second approach,we present a novel multi-class large margin classifier(MLMC). This new machine can solve K-class problems in one optimization formula-tion without increasing the size of the quadratic programming(QP) problem proportional to K. This property allows us to construct just one classifier with as few variables in the QP problem as possible to classify multi-class data,and we can gain the advantage of speed from it especially when K is large. Our experiments indicate that MLMC almost works as well as(sometimes better than) many other multi-class SVCs for some benchmark data classification problems,and obtains a reasonable performance in face recognition application on the AR face database.展开更多
文摘基于活体肝细胞的31P磁共振波谱图(31Phosphorus Magnetic Resonance Spectroscopy,31P-MRS)对肝细胞数据进行诊断,分为3种类型:肝癌、肝硬化和正常肝。分别运用线性分类器和二次分类器对数据分类,并在分类前进行了特征抽取。线性分类器和二次分类器在"留一法"中对上述3种类型的分类准确率分别约为81.37%、77.75%、92.30%和95.27%、99.89%、99.70%。实验证明二次分类器相对于线性分类器,明显地提高了分类准确率。
基金supported by the National Natural Science Foundation of China (No. 60675049)the National Creative Research Groups Science Foundation of China (No. 60721062)the Natural Science Foundation of Zhejiang Province, China (No. Y106414)
文摘Currently there are two approaches for a multi-class support vector classifier(SVC). One is to construct and combine several binary classifiers while the other is to directly consider all classes of data in one optimization formulation. For a K-class problem(K>2),the first approach has to construct at least K classifiers,and the second approach has to solve a much larger op-timization problem proportional to K by the algorithms developed so far. In this paper,following the second approach,we present a novel multi-class large margin classifier(MLMC). This new machine can solve K-class problems in one optimization formula-tion without increasing the size of the quadratic programming(QP) problem proportional to K. This property allows us to construct just one classifier with as few variables in the QP problem as possible to classify multi-class data,and we can gain the advantage of speed from it especially when K is large. Our experiments indicate that MLMC almost works as well as(sometimes better than) many other multi-class SVCs for some benchmark data classification problems,and obtains a reasonable performance in face recognition application on the AR face database.
