TY - GEN
T1 - Reasoning with classifiers
AU - Roth, Dan
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2002.
PY - 2002
Y1 - 2002
N2 - 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,th us 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.
AB - 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,th us 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.
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U2 - 10.1007/3-540-36755-1_43
DO - 10.1007/3-540-36755-1_43
M3 - Conference contribution
AN - SCOPUS:84945289213
SN - 9783540440369
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 506
EP - 510
BT - Machine Learning
A2 - Elomaa, Tapio
A2 - Mannila, Heikki
A2 - Toivonen, Hannu
PB - Springer
T2 - 13th European Conference on Machine Learning, ECML 2002
Y2 - 19 August 2002 through 23 August 2002
ER -