Reasoning with classifiers

Dan Roth

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


Research in machine learning concentrates on the study of learning single concepts from examples. In this framework the learner attempts to learn a single hidden function from a collection of examples, assumed to be drawn independently from some unknown probability distribution. However, in many cases - as in most natural language and visual processing situations - decisions depend on the outcomes of several different but mutually dependent classifiers. The classifiers' outcomes need to respect some constraints that could arise from the sequential nature of the data or other domain specific conditions, thus requiring a level of inference on top the predictions. We will describe research and present challenges related to Inference with Classifiers - a paradigm in which we address the problem of using the outcomes of several different classifiers in making coherent inferences - those that respect constraints on the outcome of the classifiers. Examples will be given from the natural language domain.

Original languageEnglish (US)
Title of host publicationPrinciples of Data Mining and Knowledge Discovery - 6th European Conference, PKDD 2002, Proceedings
EditorsTapio Elomaa, Heikki Mannila, Hannu Toivonen
Number of pages5
ISBN (Print)3540440372, 9783540440376
StatePublished - 2002
Externally publishedYes
Event6th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2002 - Helsinki, Finland
Duration: Aug 19 2002Aug 23 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2431 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other6th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2002

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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