Multicategory support vector machines: Theory and application to the classification of microarray data and satellite radiance data
成果类型：
Article
署名作者：
Lee, YK; Lin, Y; Wahba, G
署名单位：
University System of Ohio; Ohio State University; University of Wisconsin System; University of Wisconsin Madison
刊物名称：
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN/ISSBN：
0162-1459
DOI：
10.1198/016214504000000098
发表日期：
2004
页码：
67-81
关键词：
摘要：
Two-category support vector machines (SVM) have been very popular in the machine learning community for classification problems. Solving multicategory problems by a series of binary classifiers is quite common in the SVM paradigm; however, this approach may fail under various circumstances. We propose the multicategory support vector machine (MSVM), which extends the binary SVM to the multicategory case and has good theoretical properties. The proposed method provides a unifying framework when there are either equal or unequal misclassification costs. As a tuning criterion for the MSVM, an approximate leave-one-out cross-validation function, called Generalized Approximate Cross Validation, is derived. analogous to the binary case. The effectiveness of the MSVM is demonstrated through the applications to cancer classification using microarray data and cloud classification with satellite radiance profiles.