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 a...

This paper proposes a new method for extracting impervious surface from VHR imagery. Since the impervious surface is the only class of interest (i.e. target class), the One Class Support Vector Machine (OCSVM), a recently developed statistical learning method, was used as the classifier. Rather than use samples from all classes for training in traditional multi-class classification, the method ...

In this paper, a method for secondary structure with support vector machines is presented. The system used two layers of support vector machines, with a weighted cost function to balance the uneven class memberships. Using this method, prediction accuracy reaches 71.5%, comparable to the best techniques avaliable.

The standard -norm SVM is known for its good performance in twoclass classification. In this paper, we consider the -norm SVM. We argue that the -norm SVM may have some advantage over the standard -norm SVM, especially when there are redundant noise features. We also propose an efficient algorithm that computes the whole solution path of the -norm SVM, hence facilitates adaptive selection of th...

This paper describes an adaptive multi-modal person verification system based on speech and face images. The system adapts to noise present in the speech signal by modifying the parameters of the fusion module. Linear and Support Vector Machine (SVM) based techniques of fusing the similarity measures from speech and face modes are investigated. Experimental results obtained on the Digit Databas...

Most of the existing facial expression recognition methods are based on either only texture features or only geometrical features. In this paper, we propose to improve the performance of facial expression recognition by combining both types of features using fuzzy integral. The geometric features used are the displacements of positions of feature points on the face. We first embed them in a low...

Here, we propose a framework that provides a detailed analysis of the spectrotemporal modulations in the acoustic signal, augmented with a discriminative classifier using support vector machines. We have seen that such representation is successful at capturing the nontrivial commonalties within a sound class and differences between different classes[1, 2, 3].

We apply functional approximation techniques to obtain features from online data and use these features to train support vector machines (SVMs) for online mathematical symbol classification. We show experimental results and comparisons with another SVM-based system trained using features used in the literature. The experimental results show that the SVM trained using features from functional ap...

Support vector regression (SVR) has been popular in the past decade, but it provides only an estimated target value instead of predictive probability intervals. Many work have addressed this issue but sometimes the SVR formula must be modified. This paper presents a rather simple and direct approach to construct such intervals. We assume that the conditional distribution of the target value dep...

Remarkable improvements in recognition can be achieved through multibiometric fusion. Among various fusion techniques, score level fusion is the most frequently used in multibiometric system. In this paper, we propose a novel fusion algorithm based on False Reject Rate (FRR) and False Accept Rate (FAR) using Support Vector Machine (SVM). It transfers scores into corresponding FRRs and FARs, thu...