Je Schneider Andrew Moore School of Computer Science Carnegie Mellon...  Home/Search
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Abstract: Traditional design of experiments (DOE) from the statistics literature focuses on optimizing an output parameter over a space of continuous input parameters. Here we consider DOE, or active learning, for discrete input spaces. A trivial example of this is the k-armed bandit problem, which is the case of having a single input attribute of arity k. We address the full problem of many attributes where it is impossible to test every combination of attribute-value pairs even once within the... (Update)
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BibTeX entry: (Update)
@misc{ andrew-active,
author = "Je Schneider Andrew",
title = "Active Learning in Discrete Input Spaces",
url = "citeseer.ist.psu.edu/751660.html" }
Citations (may not include all citations):696 UCI repository of machine learning databases (context) - Blake, Merz - 1998
614 Reinforcement Learning: An Introduction - Sutton, Barto - 1998
121 Classi cation and Regression Trees (context) - Brieman, Friedman et al. - 1984
111 Active learning with statistical models - Cohn, Ghahramani et al. - 1994
75 Combining Instance-Based and Model-Based Learning - Quinlan - 1993
65 Bandit Problems: Sequential Allocation of Experiments (context) - Berry, Fristedt - 1985
64 Multi-Armed Bandit Allocation Indices (context) - Gittins - 1989
47 Response Surface Methodology: Process and Product Optimizati.. (context) - Myers, Montgomery - 1995
30 Inductive Learning Algorithms for Complex Systems Modeling (context) - Madala, Ivakhnenko - 1994
18 Memory based stochastic optimization - Moore, Schneider - 1995
4 Memory-based active learning for optimizing noisy continuous.. (context) - Moore, Schneider et al. - 1998
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