¡ º ¬ º ³¢è Combining Svms with Various Feature Selection Strategies § Ï×ö Ø ¼ ¼ § Á¡°¼ Ö ÷ Ç Combining Svms with Various Feature Selection Strategies

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.