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margin maximization

Margin maximization is an optimization principle in machine learning where a model selects a decision boundary that maximizes the distance between the boundary and the closest data points of different classes. Most prominently utilized in support vector machines and related classification frameworks, this approach identifies an optimal separating hyperplane that provides the widest possible geometric buffer between distinct categories or between correct predictions and competing alternative hypotheses. Grounded in statistical learning theory and structural risk minimization, maximizing this separation margin reduces the risk of overfitting and improves the model's capacity to generalize accurately to new, unseen data.

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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