Abstract
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.
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Castelli, I., Trentin, E. (2012). Semi-unsupervised Weighted Maximum-Likelihood Estimation of Joint Densities for the Co-training of Adaptive Activation Functions. In: Schwenker, F., Trentin, E. (eds) Partially Supervised Learning. PSL 2011. Lecture Notes in Computer Science(), vol 7081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28258-4_7
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DOI: https://doi.org/10.1007/978-3-642-28258-4_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28257-7
Online ISBN: 978-3-642-28258-4
eBook Packages: Computer ScienceComputer Science (R0)