The problem of inferring human emotional state automatically from speech has become one the central problems in Man Machine Interaction (MMI). Though Support Vector Machines (SVMs) were used several worksfor emotion recognition speech, potential using probabilistic SVMs for this task is not explored. emphasis current work on how to use efficient emotions speech. Emotional corpuses two Dravidian...

Support Vector Machines choose the hypothesis corresponding to the centre of the largest hypersphere that can be inscribed in version space. If version space is elongated or irregularly shaped a potentially superior approach is take into account the whole of version space. We propose to construct the Bayes point which is approximated by the centre of mass. Our implementation of a Bayes Point Ma...

Support vector machines (SVMs) are regularly used for classification of unbalanced data by weighting more heavily the error contribution from the rare class. This heuristic technique is often used to learn classifiers with high F-measure, although this particular application of SVMs has not been rigorously examined. We provide significant and new theoretical results that support this popular he...

The theory of support vector machines (SVM), has its origins in the late seventies, in the work of Vapnik [16] on the theory of statistical learning. Lately it has been receiving increasing attention, and many applications as well as important theoretical results are based on this theory. In fact, Support Vector Machines are arguably the most important discovery in the area of machine learning....

Support Vector Machines (SVMs) which is one kind of learning machines, was applied to predict the specificity of GalNAc-transferase. The examination for the self-consistency and the jackknife test of the SVMs method were tested for the training dataset (305 oligopeptides), the correct rate of self-consistency and jackknife test reaches 100% and 84.9%, respectively. Furthermore, the prediction o...

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....

The increasing interest in Support Vector Machines (SVMs) over the past 15 years is described. Methods are illustrated using simulated case studies, and 4 experimental case studies, namely mass spectrometry for studying pollution, near infrared analysis of food, thermal analysis of polymers and UV/visible spectroscopy of polyaromatic hydrocarbons. The basis of SVMs as two-class classifiers is s...

In the recent few decades there has been very significant developments in the theoretical understanding of Support vector machines (SVMs) as well as algorithmic strategies for implementing them, and applications of the approach to practical problems. SVMs introduced by Vapnik and others in the early 1990s are machine learning systems that utilize a hypothesis space of linear functions in a high...

The problem of feature selection for Support Vector Machines (SVMs) classification is investigated in the linear two classes case. We suggest a new method of feature selection based on ranking scores derived from SVMs. We analyze the retraining effects on the ranking rules based on these scores. Our features selection algorithm consists in a forward selection strategy according to the decreasin...