Cited By
View all- Zhou H(2019)Active Online Learning in the Binary Perceptron ProblemCommunications in Theoretical Physics10.1088/0253-6102/71/2/24371:2(243)Online publication date: 13-Feb-2019
Acceleration of learning in binary choice problems
Pages 446 - 452
References
[1]
Amari, S. 1967. A theory of adaptive pattern classifiers. IEEE Trans. EC, 16, 290-307.
[2]
Baum, E.B. 1991. Neural net algorithms that learn in polynomial time from examples and queries. IEEE Trans. Neural Networks 2, 5-19.
[3]
Chung, K. 1953. On a stochastic approximation method. Ann. Math. Stat. 25, 463-483.
[4]
Fabian, V. On asymptotic normality in stochastic approximation. Ann. Math. Stat. 39, 1327-1333.
[5]
Haussler, D. 1991. Decision theoretic generalization of the PAC model for neural net arid other learning application. UCSC-CRL-91-02.
[6]
Kabashima, Y., and Shinomoto, S. 1992. Learning curves for error minimum and maximum likelihood algorithms. Neural Comp. 4. 712-719.
[7]
Kabashima, Y., and Shinomoto, S. 1993. Learning a decision boundary from stochastic examples: incremental algorithms with and without queries, p~priat.
[8]
Kawanabe, M., and Amari, S. 1993. Estimation of neuronal parameters by the semiparametric statistical method, preipr/nt.
[9]
Kim, J., and Pollard, D. 1990. Cube root asymptotic. Ann. Stat. 18, 191-219.
[10]
Kohonen, T. 1989. Self-Organization and associative memory. 3rd ed. Springer-Verlag.
[11]
Kushner, H.J., and Clark, D.S. 1978. Stochastic approximation methods for constrained and unconstrained systems. Springer-Verlag.
[12]
Luo, Z.Q. 1991. On the convergence of the LMS algorithm with adaptive learning rate for linear feedforward networks. Neural Comp. 3, 226-245.
[13]
Pollard, D. 1984. Convergence of Stochastic Processes. Springer-Verlag.
[14]
Robbins, H., a~d Monro, S. 1951. A stochastic approximation method. Ann. Math. Stat. 22,400-407
[15]
Sacks, J. 1958. Asymptotic distribution of stochastic approximation procedure. Ann. Math. Stat. 29, 373- 405.
[16]
Wax, N. 1954. ed. Selected papers on noise and stochastic processes. Dover.
Index Terms
- Acceleration of learning in binary choice problems
Recommendations
Perfect binary codes: constructions, properties, and enumeration
Properties of nonlinear perfect binary codes are investigated and several new constructions of perfect codes are derived from these properties. An upper bound on the cardinality of the intersection of two perfect codes of length n is presented, and ...
Multicriteria Choice Functions Based on Binary Relations
This paper discusses methods of determining choices from sets of alternatives on the basis of one or more binary relations defined on the alternatives. Although the binary relations will be ordering relations in many practical contexts, most of the ...
Offline reinforcement learning with anderson acceleration for robotic tasks
AbstractOffline reinforcement learning (RL) can learn effective policy from a fixed batch of data without interaction. However, the real-world requirements, such as better performance and high sample efficiency, put substantial challenges on current ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Copyright © 1993 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 01 August 1993
Check for updates
Qualifiers
- Article
Conference
6COLT93: 6th Annual Conference on Computational Learning Theory
July 26 - 28, 1993
California, Santa Cruz, USA
Acceptance Rates
Overall Acceptance Rate 35 of 71 submissions, 49%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 164Total Downloads
- Downloads (Last 12 months)25
- Downloads (Last 6 weeks)5
Reflects downloads up to 26 Sep 2024
Other Metrics
Citations
Cited By
View all- Zhou H(2019)Active Online Learning in the Binary Perceptron ProblemCommunications in Theoretical Physics10.1088/0253-6102/71/2/24371:2(243)Online publication date: 13-Feb-2019
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in