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Abstract: We investigate, within the PAC learning model, the problem of learning nonoverlapping perceptron networks (also known as read-once formulas over a weighted threshold basis). These are loop-free neural nets in which each node has only one outgoing weight. We give a polynomial time algorithm that PAC learns any nonoverlapping perceptron network using examples and membership queries. The algorithm is able to identify both the architecture and the weight values necessary to represent the... (Update)
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.... positive learnability results on di#erent classes of read once Boolean formulas, a membership query algorithm has been recently proposed [4] for learning the class of nonoverlapping perceptron networks. These networks (also known as perceptron networks or read once formulas...
...neural version of read once boolean formulas. Two perceptrons are said to be nonoverlapping if they do not share any input variables [7]. Standard techniques [4] show that under an arbitrary distribution, such networks are no easier to learn than networks with overlapping...
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BibTeX entry: (Update)
T. Hancock, M. Golea, and M. Marchand. Learning nonoverlapping perceptron networks from examples and membership queries. Machine Learning 16(3): 161--183, 1994. http://citeseer.ist.psu.edu/hancock94learning.html More
@article{ hancock94learning,
author = "Thomas R. Hancock and Mostefa Golea and Mario Marchand",
title = "Learning Nonoverlapping Perceptron Networks from Examples and Membership Queries",
journal = "Machine Learning",
volume = "16",
number = "3",
pages = "161-183",
year = "1994",
url = "citeseer.ist.psu.edu/hancock94learning.html" }
Citations (may not include all citations):465 Learnability and the VapnikChervonenkis dimension (context) - Blumer, Ehrenfeucht et al. - 1989
448 A new polynomial time algorithm for linear programming (context) - Karmarkar - 1984
203 What size net gives valid generalization (context) - Baum, Haussler - 1989
184 Cryptographic limitations on learning boolean formulae and f.. - Kearns, Valiant - 1989
102 Training a 3-node neural network is NP-complete - Blum, Rivest - 1988
97 Learning read-once formulas with queries - Angluin, Hellerstein et al. - 1993
82 When won't membership queries help (context) - Angluin, Kharitonov - 1991
78 the learnability of boolean formulae - Kearns, Li et al. - 1987
43 Neural net algorithms that learn in polynomial time from exa.. (context) - Baum - 1991
26 On learning a union of halfspaces (context) - Baum
26 Learning arithmetic read-once formulas (context) - Bshouty, Hancock et al.
23 the complexity of loading shallow neural networks (context) - Judd - 1988
12 Learning probabilistic read-once formulas on product distrib.. (context) - Schapire - 1991
11 Learning 2 DNF formulas and k decision trees (context) - Hancock - 1991
8 A polynomial time algorithm that learns two hidden unit nets (context) - Baum
5 A Greedy Method for Learning DNF Functions under the Uniform.. (context) - Pagallo, Haussler - 1989
2 Statistical mechanics of multilayer neural networks (context) - Barkai, Hansel et al. - 1990
1 Storage capacity of a multilayer network with binary weights (context) - Barkai, Kanter - 1991
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