This thesis focuses on applying Support Vector Machines SVMs an algorithm founded in the framework of statistical learning theory to analyzing DNA microarray data The rst part of the thesis focuses on extensions to SVMs required for analyzing microarray data First the problem of choosing multiple parameters at once for SVMs is addressed This is used as the basis of a feature selection algorithm...

In least squares support vector machines (LS-SVMs), the optimal separating hyperplane is obtained by solving a set of linear equations instead of solving a quadratic programming problem. But since SVMs and LS-SVMs are formulated for two-class problems, unclassifiable regions exist when they are extended to multiclass problems. In this paper, we discuss fuzzy LS-SVMs that resolve unclassifiable ...

Support Vector Machines (SVM) is one such machine learning technique that learns the decision surface through a process of discrimination and has a good generalization capacity [6]. SVMs have been proven to be successful classifiers on several classical pattern recogntion problems [9, 11]. In this paper, one of the first applications of Support Vector Machines (SVM) technique for the problem of...

In this paper, a model of probability fuzzy support vector machines (PFSVMs) based on the consideration both for fuzzy clustering and probability distributions is proposed. In many applications of traditional support vector machines (SVMs), there are over-fitting problems due to the fact that SVM is sensitive to outliers or noises. In order to solve the problem, the fuzzy support vector machine...

This paper discuss a dependency analyzer employing Support Vector Machines (SVMs) for Japanese spoken language. Most conventional dependency analyzers target written texts. Thus, we use a currently available spoken language corpus and make the SVMs learn the corpus to build a dependency analyzer that targets spoken language. We used two types of corpora: one contains written language, and the o...

Least squares support vector machines (LS-SVMs), a nonlinear kemel based machine was introduced to investigate the prospects of application of this approach in modelling water vapor and carbon dioxide fluxes above a summer maize field using the dataset obtained in the North China Plain with eddy covariance technique. The performances of the LS-SVMs were compared to the corresponding models obta...

Through task decomposition and module combination, minmax modular support vector machines (M-SVMs) can be successfully used for different pattern classification tasks. Based on an equal clustering algorithm, M-SVMs can divide the training data set of the original problem into several subsets with nearly equal number of samples, and combine them to a series of balanced subproblems which can be t...

Artificial Intelligence (AI) methods are increasingly being used for problem solving. This paper concerns using AI-type learning machines for power quality problem, which is a problem of general interest to power system to provide quality power to all appliances. Electrical power of good quality is essential for proper operation of electronic equipments such as computers and PLCs. Malfunction o...

In this paper, detecting and tracking of a car is aimed using a stationary camera system. Background subtraction is used to detect motion and Kalman filter is used for tracking of a moving car. Classification of the car is accomplished utilizing Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs). In using SVMs and ANNs, some features should be extracted from Region of Interest...

Audio data is one of typical multimedia data and it contains plenty of information. Audio retrieval is becoming important content in multimedia information retrieval. In multimedia retrieval researches, it becomes more and more important research part how to construct better classifiers for audio classification and retrieval. Support Vector Machines, a novel method of the Pattern Recognition, p...