Motivation Protein remote homology prediction and fold recognition are central problems in computational biology. Supervised learning algorithms based on support vector machines are currently one of the most effective methods for solving these problem. These methods are primarily used to solve binary classification problems and they have not been extensively used to solve the more general multiclass remote homology prediction and fold recognition problems. Methods We developed a number of methods for building SVMbased multiclass classification schemes in the context of the SCOP protein classification. These methods includes schemes that directly build an SVM-based multiclass model, schemes that employ a second level learning approach to combine the predictions generated by a set of binary SVM-based classifiers, and schemes that build and combine binary classifiers for various levels of the SCOP hierarchy beyond those defining the target classes. Results We performed a comprehensive study analyzing the different approaches using four different datasets. Our results show that most of the proposed multiclass SVM-based classification approaches are quite effective in solving the remote homology prediction and fold recognition problems and that the schemes that use predictions from binary models constructed for ancestral categories within the SCOP hierarchy tend to qualitatively improve the prediction results. Website:

Even though several techniques have been proposed in the literature for achieving multiclass classification using Support Vector Machine(SVM), the scalability aspect of these approaches to handle large data sets still needs much of exploration. Core Vector Machine(CVM) is a technique for scaling up a two class SVM to handle large data sets. In this paper we propose a Multiclass Core Vector Machine(MCVM). Here we formulate the multiclass SVM problem as a Quadratic Programming(QP) problem defining an SVM with vector valued output. This QP problem is then solved using the CVM technique to achieve scalability to handle large data sets. Experiments done with several large synthetic and real world data sets show that the proposed MCVM technique gives good generalization performance as that of SVM at a much lesser computational expense. Further, it is observed that MCVM scales well with the size of the data set. 1.

Abstract. In this article, with the aim to avoid the loss of information that occurs in the usual one-versus-one SVM decomposition procedure of the two-phases (decomposition, reconstruction) multi-classification scheme tri-class SVM approach is addressed. As the most relevant result, it will be demonstrated the robustness improvement of the proposed scheme based on tri-class machine versus that based on the bi-class machine. 1

We propose a new method to partition an unlabeled dataset, called Discriminative Context Partitioning (DCP). It is motivated by the idea of splitting the dataset based only on how well the resulting parts can be separated from a context class of disjoint data points. This is in contrast to typical clustering techniques like K-means that are based on a generative model by implicitly or explicitly searching for modes in the distribution of samples. The discriminative criterion in DCP avoids the problems that density based methods have when the a priori assumption of multimodality is violated, when the number of samples becomes small in relation to the dimensionality of the feature space, or if the cluster sizes are strongly unbalanced. We formulate DCP’s separation property as a large-margin criterion, and show how the resulting optimization problem can be solved efficiently. Experiments on the MNIST and USPS datasets of handwritten digits and on a subset of the Caltech256 dataset show that, given a suitable context, DCP can achieve good results even in situation where density-based clustering techniques fail. 1.

Detecting faces across multiple views is more challenging than in a frontal view. To address this problem, an efficient approach is presented in this paper using a kernel machine based approach for learning such nonlinear mappings to provide effective view-based representation for multi-view face detection. In this paper Kernel Principal Component Analysis (KPCA) is used to project data into the view-subspaces then computed as view-based features. Multi-view face detection is performed by classifying each input image into face or non-face class, by using a two class Kernel Support Vector Classifier (KSVC). Experimental results demonstrate successful face detection over a wide range of facial variation in color, illumination conditions, position, scale, orientation, 3D pose, and expression in images from several photo collections.

Most classification problems associate a single class to each example or instance. However, there are many classification tasks where each instance can be associated with one or more classes. This group of problems represents an area known as multi-label classification. One typical example of multi-label classification problems is the classification of documents, where each document can be assigned to more than one class. This tutorial presents the most frequently used techniques to deal with these problems in a pedagogical manner, with examples illustrating the main techniques and proposing a taxonomy of multi-label techniques that highlights the similarities and differences between these techniques.