Human face recognition has become an active area of research over the last decade. The major problem of face recognition is the classification. In this paper, a new face recognition algorithm based on fusion of 2DPCA and Gabor features with SVM classifier is presented. The method first extracts features by employing Gabor wavelets followed by a face recognition algorithm 2DPCA and the SVM metho...

One of the main drawbacks of Support Vector Machines (SVM) is their high computational cost for large data sets. We propose the use of the Leader algorithm as a preprocessing procedure for SVM with large data sets, so that the obtained leaders are used as the training set for the SVM. The result is an algorithm where the Leader algorithm allows to construct a sample of the data set whose granul...

This paper proposes two methods which take advantage of k -mean clustering algorithm to decrease the number of support vectors (SVs) for the training of support vector machine (SVM). The first method uses k -mean clustering to construct a dataset of much smaller size than the original one as the actual input dataset to train SVM. The second method aims at reducing the number of SVs by which the...

This paper presents a new approach for the probability density function estimation using the Support Vector Machines (SVM) and the Expectation Maximization (EM) algorithms. In the proposed approach, an advanced algorithm for the SVM density estimation which incorporates the Mean Field theory in the learning process is used. Instead of using ad-hoc values for the parameters of the kernel functio...

In this paper, a new SVM classification algorithm is proposed, this algorithm is applied in Quantified Boolean Formulas (QBF) hybrid solving and a new QBF hybrid solver is designed. This solver apply SVM algorithm to construct inductive models and classify the formulae. At the same time, the reinforcement learning technology is applied to realize the dynamic algorithm selection. The relationshi...

This paper investigates the application of randomized algorithms for large scale SVM learning. The key contribution of the paper is to show that, by using ideas random projections, the minimal number of support vectors required to solve almost separable classification problems, such that the solution obtained is near optimal with a very high probability, is given by O(log n); if on removal of p...

The parameters of support vector machine have a great influence on the learning ability and generalization ability, so an improved ant colony optimization algorithm is proposed to optimize the parameters of SVM, then an optimized SVM classifier (IMACOSVM) is proposed for data classification. In the IMACO-SVM, the adaptive adjustment pheromone strategy is used to make relatively uniform pheromon...