A Sequential Dual Method for Structural SVMs

In many real world prediction problems the output is a structured 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 the last few years, large margin classifiers like support vector machines (SVMs) have shown much promise for structured output learning. The related optimization problem is a convex quadratic program (QP) with a large number of constraints, which makes the problem intractable for large data sets. This paper proposes a fast sequential dual method (SDM) for structural SVMs. The method makes repeated passes over the training set and optimizes the dual variables associated with one example at a time. The use of additional heuristics makes the proposed method more efficient. We present an extensive empirical evaluation of the proposed ...

• Type: Journal
• Year: 2011
• Where: SDM
• Authors: Shirish Krishnaj Shevade, Balamurugan P., S. Sundararajan, S. Sathiya Keerthi
• Keywords: Data Mining, Gradient Descent Method, SDM 2011, State Generalization, Support Vector Machines