Dynamical Learning Bias Selection

Christopher J. Merz
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:386-395, 1995.

Abstract

Determining the conditions for which a given learning algorithm is appropriate is an open problem in machine learning. Methods for selecting a learning algorithm for a given domain or for a portion of the domain have met with limited success. This paper proposes a new approach to predicting a given example’s class by locating it in the "example space" and then choosing the best learner(s) in that region of the example space to make predictions. The regions of the example space are defined by the prediction patterns of the learners being used. The learner(s) chosen for prediction are selected according to their past performance in that region. This dynamic approach to learning bias selection is compared to other methods for selecting from multiple learning algorithms.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR0-merz95a, title = {Dynamical Learning Bias Selection}, author = {Merz, Christopher J.}, booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics}, pages = {386--395}, year = {1995}, editor = {Fisher, Doug and Lenz, Hans-Joachim}, volume = {R0}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/r0/merz95a/merz95a.pdf}, url = {https://proceedings.mlr.press/r0/merz95a.html}, abstract = {Determining the conditions for which a given learning algorithm is appropriate is an open problem in machine learning. Methods for selecting a learning algorithm for a given domain or for a portion of the domain have met with limited success. This paper proposes a new approach to predicting a given example’s class by locating it in the "example space" and then choosing the best learner(s) in that region of the example space to make predictions. The regions of the example space are defined by the prediction patterns of the learners being used. The learner(s) chosen for prediction are selected according to their past performance in that region. This dynamic approach to learning bias selection is compared to other methods for selecting from multiple learning algorithms.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T Dynamical Learning Bias Selection %A Christopher J. Merz %B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1995 %E Doug Fisher %E Hans-Joachim Lenz %F pmlr-vR0-merz95a %I PMLR %P 386--395 %U https://proceedings.mlr.press/r0/merz95a.html %V R0 %X Determining the conditions for which a given learning algorithm is appropriate is an open problem in machine learning. Methods for selecting a learning algorithm for a given domain or for a portion of the domain have met with limited success. This paper proposes a new approach to predicting a given example’s class by locating it in the "example space" and then choosing the best learner(s) in that region of the example space to make predictions. The regions of the example space are defined by the prediction patterns of the learners being used. The learner(s) chosen for prediction are selected according to their past performance in that region. This dynamic approach to learning bias selection is compared to other methods for selecting from multiple learning algorithms. %Z Reissued by PMLR on 01 May 2022.
APA
Merz, C.J.. (1995). Dynamical Learning Bias Selection. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:386-395 Available from https://proceedings.mlr.press/r0/merz95a.html. Reissued by PMLR on 01 May 2022.

Related Material