頁籤選單縮合
題 名 | Multi-Category Support Vector Machines, Feature Selection and Solution Path |
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作 者 | Wang, Lifeng; Shen, Xiaotong; | 書刊名 | Statistica Sinica |
卷 期 | 16:2 民95.04 |
頁 次 | 頁617-633 |
分類號 | 319.51 |
關鍵詞 | Genome classification; Hinge loss; L₁-norm; Penalty with; Regularization; |
語 文 | 英文(English) |
英文摘要 | Support Vector Machines (SVMs) have proven to deliver high performance. However, problems remain with respect to feature selection in multi-category classification. In this article, we propose an algorithm to compute an entire regularization solution path for adaptive feature selection via L₁-norm penalized multi-category MSVM (LIMSVM). The advantages of this algorithm are three-fold. First, it permits fast computation for fine tuning, which yields accurate prediction. Second, it greatly reduces the cost of memory. This is especially important in genome classification, where a linear program with tens of thousands of variables has to be solved. Third, it yields a selection order in which the features can be examined sequentially. The performance of the proposed algorithm is examined in simulations and with data. |
本系統中英文摘要資訊取自各篇刊載內容。