Abstract : This paper is concerned with relational Support Vector Machines, at the intersection of Support Vector Machines (SVM) and relational learning or Inductive Logic Programming (ILP). The so-called phase transition framework, primarily developed for constraint satisfaction problems (CSP), has been extended to ILP, providing relevant insights into the limitations and difficulties thereof....

This work is concerned with issues that arise in implementation of support vector machines (SVM) for detecting class boundaries. First, an exact solution for the convex optimization problem for Support Vector Machines(SVM) without any conventional heuristic optimization techniques is considered. Second, the application of this solution in an iterative SVM algorithm with linear kernels to data s...

This paper investigates a novel algorithm-EGA-SVM for text classification problem by combining support vector machines (SVM) with elitist genetic algorithm (GA). The new algorithm uses EGA, which is based on elite survival strategy, to optimize the parameters of SVM. Iris dataset and one hundred pieces of news reports in Chinese news are chosen to compare EGA-SVM, GA-SVM and traditional SVM. Th...

Voice Activity Detection (VAD) algorithms based on machine learning techniques have shown competitive results in the area of automatic speech recognition. This paper describes a new approach of VAD based on Support Vector Machines (SVM) for Distributed Speech Recognition (DSR) system. In the proposed scheme, the speech and the non-speech frames are detected from the compressed Mel Frequency Cep...

This paper presents a methodology to calculate probabilities of failure using Probabilistic Support Vector Machines (PSVMs). Support Vector Machines (SVMs) have recently gained attention for reliability assessment because of several inherent advantages. Specifically, SVMs allow one to construct explicitly the boundary of a failure domain. In addition, they provide a technical solution for probl...

Outlier detection in mixed attribute datasets has proved to be a challenging task required in real world applications. Most existing algorithms for outlier detection do not consider the interactions between categorical and numerical attributes. The Pattern based Outlier Detection (POD) algorithm (Zhang & Jin, 2011), has had considerable success in the detecting outliers by analysing such intera...

Support vector machines (SVMs) are considered to be the best machine learning algorithms for minimizing the predictive probability of misclassification. However, their drawback is that for large data sets the computation of the optimal decision boundary is a time consuming function of the size of the training set. Hence several methods have been proposed to speed up the SVM algorithm. Here thre...

Parameters of support vector machines (SVM) which is optimized by standard genetic algorithm is easy to trap into the local minimum, in order to get the optimal parameters of support vector machine, this paper proposed a parameters optimization method for support vector machines based on improved genetic algorithm, the simulation experiment is carried out on 5 benchmark datasets. The simulation...