二階正規化多標籤線性分類器比較

Comparison of L2-Regularized Multi-Class Linear Classifiers

黃天亮(Tian-Liang Huang)

指導教授 ： 林智仁

國立臺灣大學/電機資訊學院/資訊工程學研究所/碩士(2010年)

https://doi.org/10.6342/NTU.2010.01911

摘要

無資料

關鍵字

線性分類模型 ； 線性支持向量機 ； 多標籤分類 ； 最大熵方法 ； 座標下降法

並列摘要

The classification problem appears in many applications such as document classification and web page search. Support vector machine(SVM) is one of the most popular tools used in classification task. One of the component in SVM is the kernel trick. We use kernels to map data into a higher dimentional space. And this technique is applied in non-linear SVMs. For large-scale sparce data, we use the linear kernel to deal with it. We call such SVM as the linear SVM. There are many kinds of SVMs in which different loss functions are applied. We call these SVMs as L1-SVM and L2-SVM in which L1-loss and L2-loss functions are used respectively. We can also apply SVMs to deal with multi-class classification with one-against-one or one-against-all approaches. In this thesis several models such as logistic regression, L1-SVM, L2-SVM, Crammer and Singer, and maximum entropy will be compared in the multi-class classification task.

並列關鍵字

linear classification ； linear support vector machines ； multi-class classification ； maximum entropy ； coordinate descent

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