Legenstein R, Maass W. (2008). On the classification capability of sign-constrained perceptrons. Neural computation. 20 [PubMed]

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References and models cited by this paper

Amit DJ, Wong KYM, Campbell C. (1989). The interaction space of neural networks with sign-constrained synapses J Phys A Math Gen. 22

Amit DJ, Wong KYM, Campell C. (1989). Perceptron learning with sign-constrained weights J Phys A Math Gen. 22

Brunel N, Hakim V, Isope P, Nadal JP, Barbour B. (2004). Optimal information storage and the distribution of synaptic weights: perceptron versus Purkinje cell. Neuron. 43 [PubMed]

Gardner E. (1987). The space of interactions in neural network models J Physics. 21

Haykin S. (1999). Neural Networks: A Comprehensive Foundation (2nd Ed).

Maass W, Bartlett PL. (2003). Vapnik-Chervonenkis dimension of neural nets The handbook of brain theory and neural networks (2nd Ed).

Maass W, Legenstein RA. (2006). Neural codes that enhance the discrimination capability of readout neurons in preparation.

Minsky M, Papert S. (1988). Perceptrons: An introduction to computational geometry.

Pitts W, Mcculloch WS. (1943). A Logical Calculus of Ideas Immanent in Nervous Activity Bull Math Biophysics. 5

Rosenblatt F. (1962). Principles Of Neurodynamics.

Strang G. (1988). Linear algebra and its application.

Vapnik V. (1998). Statistical Learning Theory.

Vapnik VN, Chervonenkis AY. (1971). On the uniform convergence of relative frequencies of events to their probabilities Theory Of Probability And Its Applications. 16

References and models that cite this paper
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