title: Compressed classification learning with Markov chain samples.
abstract: In this article, we address the problem of compressed classification learning. A generalization bound of the support vector machines (SVMs) compressed classification algorithm with uniformly ergodic Markov chain samples is established. This bound indicates that the accuracy of the SVM classifier in the compressed domain is close to that of the best classifier in the data domain. In a sense, the fact that the compressed learning can avoid the curse of dimensionality in the learning process is shown. In addition, we show that compressed classification learning reduces the learning time at the price of decreasing the classification accuracy, but the decrement can be controlled. The numerical experiments further verify the results claimed in this article.
authors: Department of Information and Mathematics Sciences, China Jiliang University, Hangzhou 310018, Zhejiang Province, PR China. Electronic address: feilongcao@gmail.com.
authors: Feilong
authors: F
authors: Cao
authors: Department of Information and Mathematics Sciences, China Jiliang University, Hangzhou 310018, Zhejiang Province, PR China.
authors: Tenghui
authors: T
authors: Dai
authors: Department of Information and Mathematics Sciences, China Jiliang University, Hangzhou 310018, Zhejiang Province, PR China.
authors: Yongquan
authors: Y
authors: Zhang
authors: Department of Information and Mathematics Sciences, China Jiliang University, Hangzhou 310018, Zhejiang Province, PR China.
authors: Yuanpeng
authors: Y
authors: Tan
mesh_terms: D000465:Algorithms; D005793:Generalization, Psychological / Q000502:physiology*; D006801:Humans; D007858:Learning / Q000145:classification* / Q000502:physiology*; D008390:Markov Chains; D010363:Pattern Recognition, Automated; D060388:Support Vector Machine
keywords: Compressed classification; Generalization error; Markov chain samples; SVM