Support Vector Machines (SVM) have gained profound interest amidst the researchers. One of the important issues concerning SVM is with its application to large data sets. It is recognized that SVM is computationally very intensive. This paper presents a novel multi SVM classification approach for large data sets using the sketch of classes distribution which is obtained by using SVM and minimum...

The binary support vector machines (SVMs) have been extensively investigated. However their extension to a multi-classification model is still an on-going research. In this paper we present an extension of the binary support vector machines (SVMs) for the k > 2 class problems. The SVM model as originally proposed requires the construction of several binary SVM classifiers to solve the multi-cla...

Support vector machines (SVMs) have become a popular tool for discriminative classification. Powerful theoretical and computational tools for support vector machines have enabled significant improvements in pattern classification in several areas. An exciting area of recent application of support vector machines is in speech processing. A key aspect of applying SVMs to speech is to provide a SV...

Run off resulted from rainfall is the main way of receiving water in most parts of the World. Therefore, prediction of runoff volume resulted from rainfall is getting more and more important in control, harvesting and management of surface water. In this research a number of machine learning and data mining methods including support vector machines, regression trees (CART algorithm), model tree...

In this paper, we compare two powerful kernel-based learning machines, support vector machines (SVM) and relevance vector machines (RVM), within the framework of hidden Markov model-based speech recognit ion. Both machines provide nonlinear discriminative classification ability: the SVM by kernelbased margin maximization and the RVM using a Bayesian probabilistic framework. The hybrid systems a...

In this paper, we propose a new algorithm called multiple physicochemical properties and support vector machines (MppS) which uses support vector machines (SVM) in conjunction with multiple physicochemical properties of amino acids. The algorithm was tested in two problems: HIV-protease and recognition of T-cell epitopes. A series of SVM classifiers combined with the ‘‘max rule’’ enables us to ...

Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algori...

In this project, two different approaches to predict Bike Sharing Demand are studied. The first approach tries to predict the exact number of bikes that will be rented using Support Vector Machines (SVM). The second approach tries to classify the demand into 5 different levels from 1 (lowest) to 5 (highest) using Softmax Regression and Support Vector Machines. Index Terms –regression, classific...

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 comparison to the original SVM, which involves a quadratic programming task; LS–SVM simplifies the required computation, but unfortunately the sparseness of standard SVM is lost. Another problem is that LS-SVM is only optimal if the training samples are corrupted by Gaussian noise. In Least Squares SVM (LS–SVM), the nonlinear solution is obtained, by first mapping the input vector to a high ...