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topic

functional dependency (functional dependencies)

A functional dependency is a relationship between attributes in a relational database where the value of one attribute or set of attributes uniquely determines the value of another. In a given table, if an attribute set X functionally determines an attribute set Y, any two records that share the same values for X must also share the same values for Y. Functional dependencies are fundamental to relational database design and normalization theory, serving as the formal basis for identifying candidate keys, eliminating data redundancy, preventing update anomalies, and preserving semantic data integrity.

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Large Margin Methods for Structured and Interdependent Output Variables

Large Margin Methods for Structured and Interdependent Output Variables

Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann, Yasemin Altun

OrganizationsCornell UniversityFraunhofer IPSIGoogleTechnische Universität DarmstadtToyota Technological Institute at Chicago

Why you should read this

Develops a generalized maximum-margin learning framework and an efficient cutting-plane algorithm that enables support vector machines to predict complex, interdependent outputs such as trees, sequences, and graphs in polynomial time.

Learning general functional dependencies between arbitrary input and output spaces is one of the key challenges in computational intelligence. While recent progress in machine learning has mainly focused on designing flexible and powerful input representations, this paper addresses the complementary issue of designing classification algorithms that can deal with more complex outputs, such as trees, sequences, or sets. More generally, we consider problems involving multiple dependent output variables, structured output spaces, and classification problems with class attributes. In order to accomplish this, we propose to appropriately generalize the well-known notion of a separation margin and derive a corresponding maximum-margin formulation. While this leads to a quadratic program with a potentially prohibitive, i.e. exponential, number of constraints, we present a cutting plane algorithm that solves the optimization problem in polynomial time for a large class of problems. The proposed method has important applications in areas such as computational biology, natural language processing, information retrieval/extraction, and optical character recognition. Experiments from various domains involving different types of output spaces emphasize the breadth and generality of our approach.

Added

2026-09-15

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