Slack and margin rescaling are variants of the structured output SVM. They define convex surrogates to task specific loss functions, which, when specialized to nonadditive loss functions for multi-label problems, yield extensions to increasing set functions. We demonstrate in this paper that we may use these concepts to define polynomial time convex extensions of arbitrary supermodular function...

This works deals with discriminant training of Gaussian Mixture Models through margin maximization. We go one step further previous work, we propose a new formulation of the learning problem that allows the use of efficient optimization algorithm popularized for Support Vector Machines, yielding improved convergence properties and recognition accuracy on handwritten digits recognition.

Leading classification methods such as support vector machines (SVMs) and their counterparts achieve strong generalization performance by maximizing the margin of separation between data classes. While the maximum margin approach has achieved promising performance, this article identifies its sensitivity to affine transformations of the data and to directions with large data spread. Maximum mar...

In this work, we propose a scalable and fast algorithm to learn a Mahalanobis distance metric. The key issue in this task is to learn an optimal Mahalanobis matrix in this distance metric. It has been shown in the statistical learning theory [?] that increasing the margin between different classes helps to reduce the generalization error. Hence, our algorithm formulates the Mahalanobis matrix a...

We study the problem of parameter estimation in continuous density hidden Markov models (CD-HMMs) for automatic speech recognition (ASR). As in support vector machines, we propose a learning algorithm based on the goal of margin maximization. Unlike earlier work on max-margin Markov networks, our approach is specifically geared to the modeling of real-valued observations (such as acoustic featu...

We study the problem of parameter estimation in continuous density hidden Markov models (CD-HMMs) for automatic speech recognition (ASR). As in support vector machines, we propose a learning algorithm based on the goal of margin maximization. Unlike earlier work on max-margin Markov networks, our approach is specifically geared to the modeling of real-valued observations (such as acoustic featu...

We present a new algorithm for multi-class classification with multiple kernels. Our algorithm is based on a natural notion of the multi-class margin of a kernel. We show that larger values of this quantity guarantee the existence of an accurate multi-class predictor and also define a family of multiple kernel algorithms based on the maximization of the multi-class margin of a kernel (MK). We p...