Add to ORKGAbstract. The statistical pattern recognition based on Bayes formula implies the concept of mutually exclusive classes. This assumption is not applicable when we have to identify some non-exclusive properties and therefore it is unnatural in biological neural networks. Considering the framework of probabilistic neural networks we propose statistical identification of non-exclusive properties by using one-class classifiers
Probabilistic networks (Bayesian networks) are suited as statistical pattern classifiers when the fe...
A combined neural network and rule-based approach is suggested as a general framework for pattern re...
Background. A significant drawback of the technology of creating modern neural network models based ...
Abstract. The statistical pattern recognition based on Bayes formula implies the concept of mutually...
summary:We summarize the main results on probabilistic neural networks recently published in a serie...
summary:For general Bayes decision rules there are considered perceptron approximations based on suf...
In this paper, two performances increasing methods for datasets which have a nonuniform class distri...
This chapter introduces a probabilistic interpretation of artificial neural networks (ANNs), moving ...
Multi-class classification problems can be efficiently solved by partitioning the original problem i...
Information retrieval in a neural network is viewed as a procedure in which the network computes a &...
This paper 1 proposes a method to extract nonlinear discriminant features from given input measure...
Providing a broad but in-depth introduction to neural network and machine learning in a statistical ...
We investigate algebraic, logical, and geometric properties of concepts recognized by various class...
159 p.As one of the Artificial Intelligence methods, Artificial Neural Networks (ANN) emerges as sig...
We study the discrimination functions associated with classifiers induced by probabilistic graphical...
Probabilistic networks (Bayesian networks) are suited as statistical pattern classifiers when the fe...
A combined neural network and rule-based approach is suggested as a general framework for pattern re...
Background. A significant drawback of the technology of creating modern neural network models based ...
Abstract. The statistical pattern recognition based on Bayes formula implies the concept of mutually...
summary:We summarize the main results on probabilistic neural networks recently published in a serie...
summary:For general Bayes decision rules there are considered perceptron approximations based on suf...
In this paper, two performances increasing methods for datasets which have a nonuniform class distri...
This chapter introduces a probabilistic interpretation of artificial neural networks (ANNs), moving ...
Multi-class classification problems can be efficiently solved by partitioning the original problem i...
Information retrieval in a neural network is viewed as a procedure in which the network computes a &...
This paper 1 proposes a method to extract nonlinear discriminant features from given input measure...
Providing a broad but in-depth introduction to neural network and machine learning in a statistical ...
We investigate algebraic, logical, and geometric properties of concepts recognized by various class...
159 p.As one of the Artificial Intelligence methods, Artificial Neural Networks (ANN) emerges as sig...
We study the discrimination functions associated with classifiers induced by probabilistic graphical...
Probabilistic networks (Bayesian networks) are suited as statistical pattern classifiers when the fe...
A combined neural network and rule-based approach is suggested as a general framework for pattern re...
Background. A significant drawback of the technology of creating modern neural network models based ...