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ILP
2007
Springer

A Phase Transition-Based Perspective on Multiple Instance Kernels

12 years 4 months ago
A Phase Transition-Based Perspective on Multiple Instance Kernels
: This paper is concerned with relational Support Vector Machines, at the intersection of Support Vector Machines (SVM) and relational learning or Inductive Logic Programming (ILP). The so-called phase transition framework, primarily developed for constraint satisfaction problems (CSP), has been extended to ILP, providing relevant insights into the limitations and difficulties thereof. The goal of this paper is to examine relational SVMs and specifically Multiple Instance-SVMs in the phase transition perspective. Introducing a relaxed CSP formalization of MI-SVMs, we first derive a lower bound on the MI-SVM generalization error in terms of the CSP satisfiability probability. Further, ample empirical evidence based on systematic experimentations demonstrates the existence of a unsatisfiability region, entailing the failure of MI-SVM approaches. Key-words : Phase Transition, Multiple Instance Learning, Relational Kernels, MI-Support Vector Machine
Romaric Gaudel, Michèle Sebag, Antoine Corn
Added 08 Jun 2010
Updated 08 Jun 2010
Type Conference
Year 2007
Where ILP
Authors Romaric Gaudel, Michèle Sebag, Antoine Cornuéjols
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