A Novel Algorithm for Learning Support Vector Machines with Structured Output Spaces

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F06%3A03124618" target="_blank" >RIV/68407700:21230/06:03124618 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

A Novel Algorithm for Learning Support Vector Machines with Structured Output Spaces

Original language description

This report proposes a novel optimization algorithm for learning support vector machines (SVM) classifiers with structured output spaces introduced recently by Tsochantaridis et. al. Learning structural SVM classifier leads to a special instance of quadratic programming (QP) optimization with a huge number of constraints. The number of constraints is proportional to the cardinality of the output space which makes the QP task intractable by classical optimization methods. We propose a novel QP solver based on sequential minimal optimization (SMO). Unlike the original SMO, we propose a novel strategy for selecting variables to be optimized. The strategy aims at selecting such variables which yield the maximal improvement of optimization. We prove that the algorithm converges in a finite number of iterations to the solution which differs from the optimal one at most by a prescribed constant.

Czech name

A Novel Algorithm for Learning Support Vector Machines with Structured Output Spaces

Czech description

This report proposes a novel optimization algorithm for learning support vector machines (SVM) classifiers with structured output spaces introduced recently by Tsochantaridis et. al. Learning structural SVM classifier leads to a special instance of quadratic programming (QP) optimization with a huge number of constraints. The number of constraints is proportional to the cardinality of the output space which makes the QP task intractable by classical optimization methods. We propose a novel QP solver based on sequential minimal optimization (SMO). Unlike the original SMO, we propose a novel strategy for selecting variables to be optimized. The strategy aims at selecting such variables which yield the maximal improvement of optimization. We prove that the algorithm converges in a finite number of iterations to the solution which differs from the optimal one at most by a prescribed constant.

Type

O - Miscellaneous

CEP classification

JD - Use of computers, robotics and its application

OECD FORD branch

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Project

<a href="/en/project/1M0567" target="_blank" >1M0567: Centre for Applied Cybernetics</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2006

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů