We consider improving the performance of k-Nearest Neighbor classifiers. A regularized kNN is proposed to learn an optimal dissimilarity function to substitute the Euclidean metric. The learning process employs hyperkernels and shares a similar regularization framework as support vector machines (SVM). Its performance is shown to be consistently better than kNN, and is competitive with SVM.

The National Weather Service (NWS) Mesocyclone Detection Algorithms (MDA) use empirical rules to process velocity data from the Weather Surveillance Radar 1988 Doppler (WSR-88D). In this study Support Vector Machines (SVM) are applied to mesocyclone detection. Comparison with other classification methods like neural networks and radial basis function networks show that SVM are more effective in...

In order to improve the efficiency and classification ability of Support vector machines (SVM) based on stochastic gradient descent algorithm, three algorithms of improved stochastic gradient descent (SGD) are used to solve support vector machine, which are Momentum, Nesterov accelerated gradient (NAG), RMSprop. The experimental results show that the algorithm based on RMSprop for solving the l...

This paper describes an on-line method for building ε-insensitive support vector machines for regression as described in (Vapnik, 1995). The method is an extension of the method developed by (Cauwenberghs & Poggio, 2000) for building incremental support vector machines for classification. Machines obtained by using this approach are equivalent to the ones obtained by applying exact methods like...

This paper describes an on-line method for building ε-insensitive support vector machines for regression as described in (Vapnik, 1995). The method is an extension of the method developed by (Cauwenberghs & Poggio, 2000) for building incremental support vector machines for classification. Machines obtained by using this approach are equivalent to the ones obtained by applying exact methods like...

In this paper we present improved training algorithms to two newly developed classifiers, reduced set vector machines and Adaboost cascade classifier applied in face detection, which are all based on learning from data. Support vector machine (SVM) has been proved to be a powerful tool for solving practical pattern recognition problems based on learning from data. Due to large number of support...

The standard support vector machines (SVM) algorithm is originally designed for two-class classification. it has been applied to solve multi-class classification problems. Several algorithms are developed for solving a multi-class problem by SVM such as one-against-one (OAO), one-against-all (OAA), and directed acyclic graph support vector machines (DAGSVM). In this research, a hybrid algorithm...

Support vector machines (SVMs) are an extremely successful class of classification and regression algorithms. Building an SVM entails the solution of a constrained convex quadratic programming problem which is quadratic in the number of training samples. Previous parallel implementations of SVM solvers sequentially solved subsets of the complete problem, which is problematic when the solution r...

Maximum margin classifiers such as Support Vector Machines (SVMs) critically depends upon the convex hulls of the training samples of each class, as they implicitly search for the minimum distance between the convex hulls . We propose Extrapolated Vector Machines (XVMs) which rely on extrapolations outside these convex hulls. XVMs improve SVM generalization very significantly on the MNIST [7] O...

Extrapolated Vector Machines. Patrick Ha ner AT&T Labs-Research, 200 Laurel Ave, Middletown, NJ 07748 [email protected] Abstract Maximum margin classi ers such as Support Vector Machines (SVMs) critically depends upon the convex hulls of the training samples of each class, as they implicitly search for the minimum distance between the convex hulls. We propose Extrapolated Vector Machines...