Publication

A Boolean Approach to Construct Neural Networks for Non-Boolean Problems

1996
Conference Papers

Abstract

A neural network construction method for problems specified for data sets with in- and/or output values in the continuous or discrete domain is described and evaluated. This approach is based on a Boolean approximation of the data set and is generic for various neural network architectures. The construction method takes advantage of a construction method for Boolean problems without increasing the dimensions of the in- or output vectors, which is a strong advantage over approaches which work on a binarized version of the data set with an increased number of in- and output elements. Further, the networks are pruned in a second phase in order to obtain very small networks.

Official source

https://infoscience.epfl.ch/entities/publication/dbcad890-025f-432e-a3b6-1ff19d9070ee

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1996
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/dbcad890-025f-432e-a3b6-1ff19d9070ee

About this result

Related concepts (32)

Artificial neural network

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

Boolean satisfiability problem

In logic and computer science, the Boolean satisfiability problem (sometimes called propositional satisfiability problem and abbreviated SATISFIABILITY, SAT or B-SAT) is the problem of determining if there exists an interpretation that satisfies a given Boolean formula. In other words, it asks whether the variables of a given Boolean formula can be consistently replaced by the values TRUE or FALSE in such a way that the formula evaluates to TRUE. If this is the case, the formula is called satisfiable.

Neural network

A neural network can refer to a neural circuit of biological neurons (sometimes also called a biological neural network), a network of artificial neurons or nodes in the case of an artificial neural network. Artificial neural networks are used for solving artificial intelligence (AI) problems; they model connections of biological neurons as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed.

Related publications (32)

A Boolean Approach to Construct Neural Networks for Non-Boolean Problems

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