Structured weight-based prediction algorithms

Akira Maruoka, Eiji Takimoto

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


Reviewing structured weight-based prediction algorithms (SWP for short) due to Takimoto, Maruoka and Vovk, we present underlying design methods for constructing a variety of on-line prediction algorithms based on the SWP. In particular, we shown how the typical expert model where the experts are considered to be arranged on one layer can be generalized to the case where they are laid on a tree structure so that the expert model can be applied to search for the best pruning in a straightforward fashion through dynamic programming scheme.

Original languageEnglish
Title of host publicationAlgorithmic Learning Theory - 9th International Conference, ALT 1998, Proceedings
EditorsMichael M. Richter, Carl H. Smith, Rolf Wiehagen, Thomas Zeugmann
PublisherSpringer Verlag
Number of pages16
ISBN (Print)354065013X, 9783540650133
Publication statusPublished - 1998
Externally publishedYes
Event9th International Conference on Algorithmic Learning Theory, ALT 1998 - Otzenhausen, Germany
Duration: Oct 8 1998Oct 10 1998

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other9th International Conference on Algorithmic Learning Theory, ALT 1998

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'Structured weight-based prediction algorithms'. Together they form a unique fingerprint.

Cite this