支向機與屬性選擇

Combining SVMs with Various Feature Selection Strategies

陳奕瑋(Yi-Wei Chen)

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(2005年)

https://doi.org/10.6342/NTU.2005.00479

摘要

在很多領域裡，屬性選擇 (feature selection) 是一件很重要的事。做屬性選擇有很多好處，例如增快執行速度、提高測試的準確度等等。本論文探討利用支向機 (Support Vector Machine) 在不同的屬性選擇策略下分類的效果。論文的前半部分主要在討論目前已有的屬性選擇方法，以及利用這些方法來參與比賽所得的經驗。後半部份則對更多的方法作深入的研究。

關鍵字

支向機 ； 支撐向量機 ； 機器學習

並列摘要

Feature selection is an important issue in many research areas. There are some reasons for selecting important features such as reducing the learning time, improving
the accuracy, etc. This thesis investigates the performance of combining support vector machines (SVM) and various feature selection strategies. The first part of the
thesis mainly describes the existing feature selection methods and our experience on using those methods to attend a competition. The second part studies more feature selection strategies using the SVM.

並列關鍵字

SVM ； feature selection ； variable selection ； Fisher

參考文獻

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