Path kernels and multiplicative updates

Eiji Takimoto, Manfred K. Warmuth

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)


We consider a natural convolution kernel defined by a directed graph. Each edge contributes an input. The inputs along a path form a product and the products for all paths are summed. We also have a set of probabilities on the edges so that the outflow from each node is one. We then discuss multiplicative updates on these graphs where the prediction is essentially a kernel computation and the update contributes a factor to each edge. Now the total outflow out of each node is not one any more. However some clever algorithms re-normalize the weights on the paths so that the total outflow out of each node is one again. Finally we discuss the use of regular expressions for speeding up the kernel and re-normalization computation. In particular we rewrite the multiplicative algorithms that predict as well as the best pruning of a series parallel graph in terms of efficient kernel computations.

Original languageEnglish
Title of host publicationComputational Learning Theory - 15th Annual Conference on Computational Learning Theory, COLT 2002, Proceedings
EditorsJyrki Kivinen, Robert H. Sloan
PublisherSpringer Verlag
Number of pages16
ISBN (Electronic)354043836X, 9783540438366
Publication statusPublished - 2002
Externally publishedYes
Event15th Annual Conference on Computational Learning Theory, COLT 2002 - Sydney, Australia
Duration: Jul 8 2002Jul 10 2002

Publication series

NameLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
ISSN (Print)0302-9743


Other15th Annual Conference on Computational Learning Theory, COLT 2002

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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