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Abstract: We present some problems with geometric characterizations that arise naturally in practical applications of machine learning. Our motivation comes from a well known machine learning problem, the problem of computing decision trees. Typically one is given a dataset of positive and negative points, and has to compute a decision tree that fits it. The points are in a low dimensional space, and the data are collected experimentally. In most practical solutions heuristic algorithms are used. To... (Update)
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BibTeX entry: (Update)
@inproceedings{ dobkin96geometric,
author = "Dobkin and Gunopulos",
title = "Geometric Problems in Machine Learning",
booktitle = "{WACG}: 1st Workshop on Applied Computational Geometry: Towards Geometric Engineering, {WACG}",
publisher = "LNCS",
year = "1996",
url = "citeseer.ist.psu.edu/136799.html" }
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6 the complexity of Optimization Problems for 3-Dimensional Co.. - Das, Goodrich - 1995
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5 Concept Learning with Geometric Hypotheses - Dobkin, Gunopulos - 1995
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4 th ACM Workshop on Computational Learning Theory (context) - Kearns, Schapire et al. - 1992
1 Low Depth Decision Trees: Algorithms and Applications (context) - Dobkin, Gunopulos et al.
1 Computing optimum shallow decision trees (context) - Dobkin, Gunopulos et al. - 1996
1 Minimizing disagreements for geometric regions using dynamic.. (context) - Kwek - 1995
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