Titlebook: Artificial Neural Networks and Machine Learning -- ICANN 2012; 22nd International C Alessandro E. P. Villa,W?odzis?aw Duch,Günther Pal Conf

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Control of a Free-Falling Cat by Policy-Based Reinforcement Learningven if the dynamics are known. To this challenge, in this study, we propose a reinforcement learning (RL) approach which enables the controller to acquire an appropriate control policy even without knowing the detailed dynamics. In particular, we focus on the control problem of a free-falling cat sy

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Gated Boltzmann Machine in Texture Modelingype of data that one can better understand by considering its local structure. For that purpose, we propose a convolutional variant of the Gaussian gated Boltzmann machine (GGBM)?[12], inspired by the co-occurrence matrix in traditional texture analysis. We also link the proposed model to a much sim

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Neural PCA and Maximum Likelihood Hebbian Learning on the GPUihood Hebbian Learning (MLHL) network designed for modern many-core graphics processing units (GPUs). The parallel implementation as well as the computational experiments conducted in order to evaluate the speedup achieved by the GPU are presented and discussed. The evaluation was done on a well-kno

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https://doi.org/10.1007/978-3-642-33266-1brain-computer interface; combinatorial optimization; evolutionary algorithm; particle swarm; self-organ