Introduction to the theory of neural computation
Introduction to the theory of neural computation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Supervised and unsupervised co-training of adaptive activation functions in neural nets
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
Supervised and unsupervised co-training of adaptive activation functions in neural nets
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
Pattern Recognition Letters
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The paper presents an explicit maximum-likelihood algorithm for the estimation of the probabilistic-weighting density functions that are associated with individual adaptive activation functions in neural networks. A partially unsupervised technique is devised which takes into account the joint distribution of input features and target outputs. Combined with the training algorithm introduced in the companion paper [2], the solution proposed herein realizes a well-defined, specific instance of the novel learning machine. The extension of the overall training method to more-than-one hidden layer architectures is pointed out, as well. A preliminary experimental demonstration is given, outlining how the algorithm works.