Learning Binary Perceptrons Perfectly Efficiently
نویسندگان
چکیده
منابع مشابه
On Learning Perceptrons with Binary Weights
We present an algorithm that PAC learns any perceptron with binary weights and arbitrary threshold under the family of product distributions. The sample complexity of this algorithm is of O((n/2)4 ln(n/δ)) and its running time increases only linearly with the number of training examples. The algorithm does not try to find an hypothesis that agrees with all of the training examples; rather, it c...
متن کاملConvergence of stochastic learning in perceptrons with binary synapses.
The efficacy of a biological synapse is naturally bounded, and at some resolution, and is discrete at the latest level of single vesicles. The finite number of synaptic states dramatically reduce the storage capacity of a network when online learning is considered (i.e., the synapses are immediately modified by each pattern): the trace of old memories decays exponentially with the number of new...
متن کاملPhysRevE Convergence of stochastic learning in perceptrons with binary synapses
The efficacy of a biological synapse is naturally bounded, and at some resolution, latest at the level of single vesicles, it is discrete. The finite number of synaptic states dramatically reduce the storage capacity of a network when online learning is considered (i.e. the synapses are immediately modified by each pattern): the trace of old memories decays exponentially with the number of new ...
متن کاملLearning Sparse Perceptrons
We introduce a new algorithm designed to learn sparse perceptrons over input representations which include high-order features. Our algorithm, which is based on a hypothesis-boosting method, is able to PAC-learn a relatively natural class of target concepts. Moreover, the algorithm appears to work well in practice: on a set of three problem domains, the algorithm produces classifiers that utili...
متن کاملBounds on the Degree of High Order Binary Perceptrons Bounds on the Degree of High Order Binary Perceptrons
High order perceptrons are often used in order to reduce the size of neural networks. The complexity of the architecture of a usual multilayer network is then turned into the complexity of the functions performed by each high order unit and in particular by the degree of their polynomials. The main result of this paper provides a bound on the degree of the polynomial of a high order perceptron,...
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ژورنال
عنوان ژورنال: Journal of Computer and System Sciences
سال: 1996
ISSN: 0022-0000
DOI: 10.1006/jcss.1996.0028