PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A phase transition-based perspective on multiple instance kernels
Romaric Gaudel, Michele Sebag and Antoine Cornuéjols
In: CAp 2007, 2-6 July 2007, Grenoble, France.

Abstract

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.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:3689
Deposited By:Romaric Gaudel
Deposited On:14 February 2008