Combining SVMs with Various Feature Selection Strategies
Date Issued
2005
Date
2005
Author(s)
Chen, Yi-Wei
DOI
en-US
Abstract
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.
Subjects
支向機
支撐向量機
機器學習
SVM
feature selection
variable selection
Fisher
Type
thesis
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