Exploiting random walks for learning
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Approximation and Estimation Bounds for Artificial Neural Networks
Machine Learning - Special issue on computational learning theory
A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems
A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems
Application of learning theory in neural modeling of dynamic systems
Application of learning theory in neural modeling of dynamic systems
A Markovian extension of Valiant's learning model
SFCS '90 Proceedings of the 31st Annual Symposium on Foundations of Computer Science
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The PAC learning theory creates a framework to assess the learning properties of models such as the required size of the training samples and the similarity between the training and training performances. These properties, along with stochastic stability, form the main characteristics of a typical dynamic ARX modeling using neural networks. In this paper, an extension of PAC learning theory is defined which includes ARX modeling tasks, and then based on the new learning theory the learning properties of a family of neural ARX models are evaluated. The issue of stochastic stability of such networks is also addressed. Finally, using the obtained results, a cost function is proposed that considers the learning properties as well as the stochastic stability of a sigmoid neural network and creates a balance between the testing and training performances.