MOTIVATION Despite many years of research on how to properly align sequences in the presence of sequencing errors, alternative splicing and micro-exons, the correct alignment of mRNA sequences to genomic DNA is still a challenging task. RESULTS We present a novel approach based on large margin learning that combines accurate splice site predictions with common sequence alignment techniques. B...

The purpose of this paper is to investigate statistical properties of risk minimization based multicategory classification methods. These methods can be considered as natural extensions of binary large margin classification. We establish conditions that guarantee the consistency of classifiers obtained in the risk minimization framework with respect to the classification error. Examples are pro...

Continuous density hidden Markov models (CD-HMMs) are an essential component of modern systems for automatic speech recognition (ASR). These models assign probabilities to the sequences of acoustic feature vectors extracted by signal processing of speech waveforms. In this chapter, we investigate a new framework for parameter estimation in CD-HMMs. Our framework is inspired by recent parallel t...

Due to the nature of complex NLP problems, structured prediction algorithms have been important modeling tools for a wide range of tasks. While there exists evidence showing that linear Structural Support Vector Machine (SSVM) algorithm performs better than structured Perceptron, the SSVM algorithm is still less frequently chosen in the NLP community because of its relatively slow training spee...

The one nearest neighbor (1-NN) rule uses instance proximity followed by class labeling information for classifying new instances. This paper presents a framework for studying properties of the training set related to proximity and labeling information, in order to improve the performance of the 1-NN rule. To this aim, a so-called class conditional nearest neighbor (c.c.n.n.) relation is introd...

We consider the problem of learning an unknown large-margin halfspace in the context of parallel computation, giving both positive and negative results. As our main positive result, we give a parallel algorithm for learning a large-margin halfspace, based on an algorithm of Nesterov’s that performs gradient descent with a momentum term. We show that this algorithm can learn an unknown γ-margin ...

In classification, semisupervised learning usually involves a large amount of unlabeled data with only a small number of labeled data. This imposes a great challenge in that it is difficult to achieve good classification performance through labeled data alone. To leverage unlabeled data for enhancing classification, this article introduces a large margin semisupervised learning method within th...

We propose a thresholded ensemble model for ordinal regression problems. The model consists of a weighted ensemble of confidence functions and an ordered vector of thresholds. We derive novel largemargin bounds of common error functions, such as the classification error and the absolute error. In addition to some existing algorithms, we also study two novel boosting approaches for constructing ...