Balamurugan P.

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In many real world prediction problems the output is a struc-tured object like a sequence or a tree or a graph. Such problems range from natural language processing to computational biology or computer vision and have been tackled using algorithms, referred to as structured output learning algorithms. We consider the problem of structured classification. In(More)
—Structural Support Vector Machines (SSVMs) have recently gained wide prominence in classifying structured and complex objects like parse-trees, image segments and Part-of-Speech (POS) tags. Typical learning algorithms used in training SSVMs result in model parameters which are vectors residing in a large-dimensional feature space. Such a high-dimensional(More)
The task of assigning label sequences to a set of observed sequences is common in computational linguistics. Several models for sequence labeling have been proposed over the last few years. Here, we focus on discriminative models for sequence labeling. Many batch and online (updating model parameters after visiting each example) learning algorithms have(More)
Several machine learning problems arising in natural language processing can be modeled as a sequence labeling problem. We provide Gaussian process models based on pseudo-likelihood approximation to perform sequence labeling. Gaus-sian processes (GPs) provide a Bayesian approach to learning in a kernel based framework. The pseudo-likelihood model enables(More)
We consider convex-concave saddle-point problems where the objective functions may be split in many components, and extend recent stochastic variance reduction methods (such as SVRG or SAGA) to provide the first large-scale linearly conver-gent algorithms for this class of problems which are common in machine learning. While the algorithmic extension is(More)
In structured output learning, obtaining labeled data for real-world applications is usually costly, while unlabeled examples are available in abundance. Semisupervised structured classification deals with a small number of labeled examples and a large number of unlabeled structured data. In this work, we consider semisupervised structural support vector(More)
In structured output learning, obtaining labeled data for real-world applications is usually costly, while unlabeled examples are available in abundance. Semi-supervised structured classification has been developed to handle large amounts of unlabeled struc-tured data. In this work, we consider semi-supervised structural SVMs with domain constraints. The(More)
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