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Generalized perceptron learning rule and its implications for photorefractive neural networks
1994
Journal of the Optical Society of America. B, Optical physics
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A mathematical proof is given that shows the conditional convergence of the learning algorithm. ...
We consider the properties of a generalized perceptron learning network, taking into account the decay or the gain of the weight vector during the training stages. ...
ACKNOWLEDGMENTS The research is supported by the National Science Council, Taiwan ...
doi:10.1364/josab.11.001619
fatcat:q4yonpdoy5detede4niqcirrya
On-line Learning of Dichotomies
1994
Neural Information Processing Systems
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The learning curve, or generalization error as a function of P, depends on the schedule at which the learning rate is lowered. ...
The performance of on-line algorithms for learning dichotomies is studied. In on-line learning, the number of examples P is equivalent to the learning time, since each example is presented only once. ...
HS is partially supported by the Fund for Basic Research of the Israeli Academy of Arts and Sciences. ...
dblp:conf/nips/BarkaiSS94
fatcat:2zmdtrdst5fx7n2opihirf2fvi
Learning rate and attractor size of the single-layer perceptron
2007
Physical Review E
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We study the simplest possible order one single-layer perceptron with two inputs, using the delta rule with online learning, in order to derive closed form expressions for the mean convergence rates. ...
We also demonstrate that the learning rate is determined by the attractor size, and that the attractors of a single-layer perceptron with N inputs partition R N R N . ...
ACKNOWLEDGMENTS The authors acknowledge fruitful discussions with B. Reznick, G. Foster, and P. Fleck. This research was supported by National Science Foundation Grant No. ...
doi:10.1103/physreve.75.026704
pmid:17358448
fatcat:h3oyd5x4s5asbaglc6kfcxx26y
Evidence that Incremental Delta-Bar-Delta Is an Attribute-Efficient Linear Learner
[chapter]
2002
Lecture Notes in Computer Science
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The Winnow class of on-line linear learning algorithms [10, 11] was designed to be attribute-efficient. ...
When learning with many irrelevant attributes, Winnow makes a number of errors that is only logarithmic in the number of total attributes, compared to the Perceptron algorithm, which makes a nearly linear ...
versions of this paper. ...
doi:10.1007/3-540-36755-1_12
fatcat:vqyv23s4mjhitfdk3ubtmtb5di
On-line AdaTron learning of unlearnable rules
1997
Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics
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We study the on-line AdaTron learning of linearly non-separable rules by a simple perceptron. ...
Optimization of the learning rate is shown to greatly improve the performance of the AdaTron algorithm, leading to the best possible generalization error for a wide range of the parameter which controls ...
ACKNOWLEDGMENTS The authors would like to thank Dr. Yoshiyuki Kabasahima for helpful suggestions and comments. One of the authors ͑J.I.͒ thanks Dr. Siegfried Bo ¨s for several useful comments. ...
doi:10.1103/physreve.55.4544
fatcat:lbgaefgufrcqnhm6r5sfe3wxsu
On-line Gibbs Learning
1996
Physical Review Letters
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The asymptotic rate of convergence is similar to that of batch learning. Constructing a general model of on-line learning is an important challenge in the theory of learning and its application. ...
For a sufficiently small learning rate, it converges to a local minimum of e g ͑w͒, but not necessarily to the global one. ...
For a sufficiently small learning rate, it converges to a local minimum of e g ͑w͒, but not necessarily to the global one. ...
doi:10.1103/physrevlett.76.3021
pmid:10060850
fatcat:3ornu7voorgzngqmxripsfoide
On-Chip Compensation of Device-Mismatch Effects in Analog VLSI Neural Networks
2004
Neural Information Processing Systems
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Our techniques enable large-scale analog VLSI neural networks with learning performance on the order of 10 bits. ...
We demonstrate our techniques on a 64-synapse linear perceptron learning with the Least-Mean-Squares (LMS) algorithm, and fabricated in a 0.35µm CMOS process. ...
Acknowledgements This work was financed in part by the Chilean government through FONDECYT grant #1040617. We fabricated our chips through MOSIS. ...
dblp:conf/nips/FigueroaBD04
fatcat:pi2zsa2nsfdd5mpaibhwfjakvm
On Herding and the Perceptron Cycling Theorem
2010
Neural Information Processing Systems
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It is shown that both algorithms can be viewed as an application of the perceptron cycling theorem. ...
perceptron and the discriminative RBM. ...
LvdM acknowledges support by the Netherlands Organisation for Scientific Research (grant no. 680.50.0908) and by EU-FP7 NoE on Social Signal Processing (SSPNet). ...
dblp:conf/nips/GelfandCMW10
fatcat:wnlmsamijrhtdobq3poymiu4ze
On-line Learning of Perceptron from Noisy Data by One and Two Teachers
2006
Journal of the Physical Society of Japan
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We analyze the on-line learning of a Perceptron from signals produced by a single Perceptron suffering from external noise or by two independent Perceptrons without noise. ...
In the single-teacher case, in order to improve the learning when it does not succeed in the sense that the student vector does not converge to the teacher vector, we use two methods: a method based on ...
On the other hand, in the Perceptron and AdaTron rules, learning fails, but using the optimal learning rate, we proved that ! ! 1 as t ! 1 in the three learning rules. ...
doi:10.1143/jpsj.75.114007
fatcat:owuo4bnlf5aofkcotmoxu2xsvm
Learning curves of the clipped Hebb rule for networks with binary weights
1993
Journal of Physics A: Mathematical and General
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In particular, the generalization rates converge extremely rapidly, oflen exponentially, to perfect genedimion. ...
These results are very encouraging given the simplicity of the learning rule, The analytic expression of the leaming curves are in excellent agreement with the numerical simulations ...
We thank the anonymous referees for their helpful comments. MG would like to thank Sara Solla for helpful suggestions. ...
doi:10.1088/0305-4470/26/21/015
fatcat:tlrlbrghancbtfagbwnygpsfje
Alternate Learning Algorithm on Multilayer Perceptrons
[chapter]
2006
Lecture Notes in Computer Science
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Multilayer perceptrons have been applied successfully to solve some difficult and diverse problems with the backpropagation learning algorithm. ...
However, the algorithm is known to have slow and false convergence aroused from flat surface and local minima on the cost function. ...
This research was supported by the Ministry of Information and Communication, Korea, under the Information Technology Research Center support program supervised by the Institute of Information Technology ...
doi:10.1007/11758501_13
fatcat:mkygfugjzvb65olzuk22gavg7a
Towards Easier and Faster Sequence Labeling for Natural Language Processing: A Search-based Probabilistic Online Learning Framework (SAPO)
[article]
2018
arXiv
pre-print
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The other is the search-based learning methods such as structured perceptron and margin infused relaxed algorithm (MIRA), which have fast training but also drawbacks: low accuracy, no probabilistic information ...
One is the probabilistic gradient-based methods such as conditional random fields (CRF) and neural networks (e.g., RNN), which have high accuracy but drawbacks: slow training, and no support of search-based ...
Acknowledgements We thank the anonymous reviewers for their thoughtful comments. This work was supported in part by National Natural Science Foundation of China (No. 61673028). ...
arXiv:1503.08381v4
fatcat:c22t6qkfdza3bjn3czm2f4mi4e
Matrix updates for perceptron training of continuous density hidden Markov models
2009
Proceedings of the 26th Annual International Conference on Machine Learning - ICML '09
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We experiment with several forms of updates, systematically comparing the effects of different matrix factorizations, initializations, and averaging schemes on phone accuracies and convergence rates. ...
Our results show that certain types of perceptron training yield consistently significant and rapid reductions in phone error rates. ...
Fei Sha is partially supported by the Charles Lee Powell Foundation. We thank the reviewers for many useful comments. ...
doi:10.1145/1553374.1553394
dblp:conf/icml/ChengSS09
fatcat:fy66c5mkcngw3lnfbwxmd4hkhq
Average case analysis of the clipped Hebb rule for nonoverlapping perception networks
1993
Proceedings of the sixth annual conference on Computational learning theory - COLT '93
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We find that the learning curves converge exponentially rapidly to perfect generalization. These results are very encouraging given the simplicity of the learning rule. ...
Using the central limit theorem and very simple counting arguments, we calculate exactly its learning curves (i.e. the generalization rates as a function of the number of training examples) in the limit ...
Specifically, the generalization rates converge exponentially to perfect generalization as a function of the number of training examples. ...
doi:10.1145/168304.168323
dblp:conf/colt/GoleaM93
fatcat:epd6qrwbxzabvljfhkueoboldy
Second-order asymmetric BAM design with a maximal basin of attraction
2003
IEEE transactions on systems, man and cybernetics. Part A. Systems and humans
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He was a Research and Design Engineer and Manager in the CNC field for five years with Victor Machinery Co. in Taiwan. ...
Comparison of recall rates of SOABAM design by the adaptive local rule and the adaptive perceptron learning are summarized in Table VI. ...
constant step size learning one, for example, perceptron learning [9] . ...
doi:10.1109/tsmca.2003.811505
fatcat:wlugi2i5lvgyvock6c4xoqqleq
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