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Abstract: Goldberg, Goldman, and Scott demonstrated how the problem of recognizing a landmark from a one-dimensional visual image can be mapped to that of learning a one-dimensional geometric pattern and gave a PAC algorithm to learn that class. In this paper, we present an efficient on-line agnostic learning algorithm for learning the class of constant-dimension geometric patterns. Our algorithm can tolerate both classification and attribute noise. By working in higher dimensional spaces we can... (Update)
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...dissertation have already been published. Chapter 3 [35] and Chapter 4 [39] have been accepted to the journal Machine Learning. Chapter 5 [38] and Chapter 7 [26] have appeared in conference proceedings and the full versions are under development. The work of Chapter 8 is still...
.... of groups such that given a single member of each group, the total weight contribution of that group can be e#ciently computed [14, 6, 7, 8, 18, 20]. But many WM and Winnow applications do not appear to exhibit such structure, so it seems that a brute force implementation is...
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BibTeX entry: (Update)
Goldman, S. & Kwek, S. & Scott, S. (1997). Agnostic Learning of Geometric Patterns. To appear in the Proceedings of the Tenth Annual Conference on Computational Learning Theory. http://citeseer.ist.psu.edu/goldman97agnostic.html More
@article{ goldman01agnostic,
author = "Sally A. Goldman and Stephen Kwek and Stephen D. Scott",
title = "Agnostic Learning of Geometric Patterns",
journal = "Journal of Computer and System Sciences",
volume = "62",
number = "1",
pages = "123--151",
year = "2001",
url = "citeseer.ist.psu.edu/goldman97agnostic.html" }
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261 The weighted majority algorithm - Littlestone, Warmuth - 1994
183 Solving multiclass learning problems via error-correcting ou.. - Dietterich, Bakiri - 1995
144 Computational limitations on learning from examples (context) - Pitt, Valiant - 1988
107 Efficient noise-tolerant learning from statistical queries - Kearns - 1993
102 Toward efficient agnostic learning - Kearns, Schapire et al. - 1994
84 Learning disjunctions of conjunctions (context) - Valiant - 1985
81 Equivalence of models for polynomial learnability (context) - Haussler, Kearns et al. - 1991
79 Qualitative navigation for mobile robots (context) - Levitt, Lawton - 1990
79 Solving the multiple-instance problem with axis-parallel rec.. - Dietterich, Lathrop et al. - 1996
64 and linear threshold learning using Winnow (context) - Littlestone, attributes et al. - 1991
54 Error-correcting output codes: A general method for improvin.. - Dietterich, Bakiri - 1991
36 Efficient learning with virtual threshold gates - Maass, Warmuth - 1998
32 From on-line to batch learning (context) - Littlestone - 1989
27 Using output codes to boost multiclass learning problems - Schapire - 1997
27 logarithmic mistake bounds when few input variables are rele.. (context) - Kivinen, Warmuth et al. - 1998
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17 Specification and simulation of statistical query algorithms.. - Aslam, Decatur - 1998
16 Approximation of convex bodies (context) - Gruber - 1983
12 Visual control of autonomous mobile robot based on selforgan.. (context) - Suzuki, Arimoto - 1988
12 Exact learning of discretized geometric concepts - Bshouty, Goldberg et al. - 1998
12 Converting English text to speech: A machine learning approa.. (context) - Bakiri - 1991
8 Error-correcting output coding corrects bias and variance - Kong, Dietterich - 1995
8 PAC Learning Geometrical Figures (context) - Goldberg - 1992
8 A theoretical and empirical study of a noise-tolerant algori.. - Goldman, Scott - 1998
7 IEEE Control Systems Magazine (context) - Hong, Tan et al. - 1992
7 Image-Based Navigation Through Large-Scaled Environments (context) - Pinette - 1993
6 PAC learning of one-dimensional patterns - Goldberg, Goldman et al. - 1996
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