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support vector machine learning

Support vector machine learning is a supervised machine learning paradigm used for classification, regression, and structured prediction by finding an optimal decision boundary, known as a hyperplane, that maximizes the margin of separation between distinct classes or target values. In this approach, the algorithm identifies the most critical training points, called support vectors, which lie closest to the boundary and determine its geometric orientation. By utilizing mathematical functions called kernels, support vector machine learning can implicitly map input data into higher-dimensional spaces, allowing it to efficiently resolve complex non-linear relationships. This framework provides robust generalization capabilities, making it effective for high-dimensional data, multiclass categorization, and interdependent or structured output tasks.

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Support vector machine learning for interdependent and structured output spaces

Support vector machine learning for interdependent and structured output spaces

Ioannis Tsochantaridis, Thomas Hofmann, Thorsten Joachims, Yasemin Altun

OrganizationsBrown UniversityCornell University

Why you should read this

Presents a maximum-margin framework that extends Support Vector Machines to complex, structured prediction tasks and solves the resulting exponential-sized optimization problem via an efficient cutting-plane algorithm.

Learning general functional dependencies is one of the main goals in machine learning. Recent progress in kernel-based methods has focused on designing flexible and powerful input representations. This paper addresses the complementary issue of problems involving complex outputs such as multiple dependent output variables and structured output spaces. We propose to generalize multiclass Support Vector Machine learning in a formulation that involves features extracted jointly from inputs and outputs. The resulting optimization problem is solved efficiently by a cutting plane algorithm that exploits the sparseness and structural decomposition of the problem. We demonstrate the versatility and effectiveness of our method on problems ranging from supervised grammar learning and named-entity recognition, to taxonomic text classification and sequence alignment.

Added

2026-09-25