AbstractThe even-odd parity problem is a tough one for neural networks to handle because they assume a finite dimensional vector space. Typically, the size of the neural network increases as the size of the problem increases. The triple parity problem is even tougher. In this paper, a method is proposed for supervised and unsupervised learning to classify bit strings of arbitrary length in terms of their triple parity. The learner is modeled by two formal concepts, transformation system and stability optimization. Even though a small set of short examples were used in the training stage, all bit strings of any length were classified correctly in the online recognition stage. The proposed learner has successfully learned to devise a way by m...
We study the problem of learning parity functions that depend on at most k variables (k-parities) at...
We show that neural network classifiers with single-layer training can be applied efficiently to com...
In this work we present neural network train-ing algorithms, which are based on the differ-ential ev...
AbstractThe even-odd parity problem is a tough one for neural networks to handle because they assume...
Abstract:- Highly nonlinear data sets are important in the field of artificial neural networks. It i...
Starting with two hidden units, we train a simple single hidden layer feed-forward neural network to...
An algorithm for the training of multilayered feedforward neural networks is presented. The strategy...
Human beings have the ability to apply the domain knowledge learned from a smaller problem to more c...
An algorithm for the training of a special multilayered feed-forward neural network is presented. Th...
Abstract. A universal binary neuron (UBN) operates with the complex-valued weights and the complex-v...
A universal binary neuron (UBN) operates with complex-valued weights and a complex-valued activation...
www.elsevier.com/locate/neucom N-bit parity neural networks:new solutions based on linear programmin
In this work, we study how the selection of examples affects the learning procedure in a neural netw...
Many machine learning algorithms are based on the similarity or distance between objects. For these ...
In this paper, we aim to learn a mapping (or embedding) from images to a compact binary space in whi...
We study the problem of learning parity functions that depend on at most k variables (k-parities) at...
We show that neural network classifiers with single-layer training can be applied efficiently to com...
In this work we present neural network train-ing algorithms, which are based on the differ-ential ev...
AbstractThe even-odd parity problem is a tough one for neural networks to handle because they assume...
Abstract:- Highly nonlinear data sets are important in the field of artificial neural networks. It i...
Starting with two hidden units, we train a simple single hidden layer feed-forward neural network to...
An algorithm for the training of multilayered feedforward neural networks is presented. The strategy...
Human beings have the ability to apply the domain knowledge learned from a smaller problem to more c...
An algorithm for the training of a special multilayered feed-forward neural network is presented. Th...
Abstract. A universal binary neuron (UBN) operates with the complex-valued weights and the complex-v...
A universal binary neuron (UBN) operates with complex-valued weights and a complex-valued activation...
www.elsevier.com/locate/neucom N-bit parity neural networks:new solutions based on linear programmin
In this work, we study how the selection of examples affects the learning procedure in a neural netw...
Many machine learning algorithms are based on the similarity or distance between objects. For these ...
In this paper, we aim to learn a mapping (or embedding) from images to a compact binary space in whi...
We study the problem of learning parity functions that depend on at most k variables (k-parities) at...
We show that neural network classifiers with single-layer training can be applied efficiently to com...
In this work we present neural network train-ing algorithms, which are based on the differ-ential ev...