The problem of ranking a set of visual samples according to their relevance to a query plays an important role in computer vision. The traditional approach for ranking is to train a binary classifier such as a support vector machine (svm). Binary classifiers suffer from two main deficiencies: (i) they do not optimize a ranking-based loss function, for example, the average precision (ap) loss; a...

Motivation: The function of an unknown biological sequence can often be accurately inferred if we are able to map this unknown sequence to its corresponding homologous family. Currently, discriminative approach which combines support vector machine and sequence similarity is recognized as the most accurate approach. SVM-Fisher and SVM-pairwise methods are two representatives of this approach, a...

In this work we address the Eν–SVM model proposed by Pérez–Cruz et al. as an extension of the traditional ν support vector classification model (ν–SVM). Through an enhancement of the range of admissible values for the regularization parameter ν, the Eν–SVM has been shown to be able to produce a wider variety of decision functions, giving rise to a better adaptability to the data. However, while...

Web catalog integration is an emerging problem in current digital content management. Past studies show that more improvement on integration accuracy can be achieved with advanced classifiers. Because Support Vector Machine (SVM) has shown its supremeness in recent research, we propose an iterative SVM-based approach (SVM-IA) to improve the integration performance. We have conducted experiments...

Support Vector Machine(SVM) is a powerful classifier used successfully in many pattern recognition problems. Furthermore, the good performance of SVM classifier has been shown in handwriting recognition field. Least Squares SVM, like SVM, is based on the marginmaximization principle performing structural risk, but its training is easier: it is only needed to solve a convex linear problem rather...

In this paper we introduce the use of semi-supervised support vector machines for rainfall estimation using images obtained from visible and infrared NOAA satellite channels. Two experiments were performed, one involving traditional SVM and other using semi-supervised SVM (SVM). The SVM approach outperforms SVM in our experiments, with can be seen as a good methodology for rainfall satellite es...

We show how the SVM can be viewed as a maximum likelihood estimate of a class of probabilistic models. This model class can be viewed as a reparametrization of the SVM in a similar vein to the ν-SVM reparametrizing the classical (C-)SVM. It is not discriminative, but has a non-uniform marginal. We illustrate the benefits of this new view by rederiving and re-investigating two established SVM-re...

In this paper we present a new algorithm to speed up the training time of Support Vector Machines (SVM). SVM has some important properties like solid mathematical background and a better generalization capability than other machines like for example neural networks. On the other hand, the major drawback of SVM occurs in its training phase, which is computationally expensive and highly dependent...

In this tutorial we present a brief introduction to SVM, and we discuss about SVM from published papers, workshop materials & material collected from books and material available online on the World Wide Web. In the beginning we try to define SVM and try to talk as why SVM, with a brief overview of statistical learning theory. The mathematical formulation of SVM is presented, and theory for the...