Gaussian mixture models (GMM), trained using the generative criterion of maximum likelihood estimation, have been the most popular approach in speaker recognition during the last decades. This approach is also widely used in many other classification tasks and applications. Generative learning in not however the optimal way to address classification problems. In this paper we first present a ne...

In this paper, we present a supervised learning approach to training submodular scoring functions for extractive multidocument summarization. By taking a structured prediction approach, we provide a large-margin method that directly optimizes a convex relaxation of the desired performance measure. The learning method applies to all submodular summarization methods, and we demonstrate its effect...

We present a supervised model for tensor dimensionality reduction, which is called large margin low rank tensor analysis (LMLRTA). In contrast to traditional vector representation-based dimensionality reduction methods, LMLRTA can take any order of tensors as input. And unlike previous tensor dimensionality reduction methods, which can learn only the low-dimensional embeddings with a priori spe...

Various problems in nonnegative quadratic programming arise in the training of large margin classifiers. We derive multiplicative updates for these problems that converge monotonically to the desired solutions for hard and soft margin classifiers. The updates differ strikingly in form from other multiplicative updates used in machine learning. In this paper, we provide complete proofs of conver...

We present a formulation of deep learning that aims at producing a large margin classifier. The notion of margin, minimum distance to a decision boundary, has served as the foundation of several theoretically profound and empirically successful results for both classification and regression tasks. However, most large margin algorithms are applicable only to shallow models with a preset feature ...

The active selection of instances can sig-niicantly improve the generalisation performance of a learning machine. Large margin classiiers such as support vector machines classify data using the most informative instances (the support vectors). This makes them natural candidates for instance selection strategies. In this paper we propose an algorithm for the training of support vector machines u...

Multilabel classification is a central problem in many areas of data analysis, including text and multimedia categorization, where individual data objects need to be assigned multiple labels. A key challenge in these tasks is to learn a classifier that can properly exploit label correlations without requiring exponential enumeration of label subsets during training or testing. We investigate no...

Graph as a common structure of machine learning, has played an important role in many learning tasks such as graph-based semi-supervised learning (GSSL). The quality of graph, however, seriously affects the performance of GSSL; moreover, an inappropriate graph may even cause deteriorated performance, that is, GSSL using unlabeled data may be outperformed by direct supervised learning with only ...

Despite the success of the popular kernelized support vector machines, they have two major limitations: they are restricted to Positive Semi-Definite (PSD) kernels, and their training complexity scales at least quadratically with the size of the data. Many natural measures of similarity between pairs of samples are not PSD e.g. invariant kernels, and those that are implicitly or explicitly defi...

We consider using online large margin classification algorithms in a setting where the target classifier may change over time. The algorithms we consider are Gentile’s Alma, and an algorithm we call Norma which performs a modified online gradient descent with respect to a regularised risk. The update rule of Alma includes a projectionbased regularisation step, whereas Norma has a weight decay t...