Titlebook: Neural Information Processing; 21st International C Chu Kiong Loo,Keem Siah Yap,Kaizhu Huang Conference proceedings 2014 Springer Internati

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Posterior Distribution Learning (PDL): A Novel Supervised Learning Frameworkt well labeled and uniformly distributed samples. However, in many real applications, the cost of labeled samples is generally very expensive. How to make use of ample easily available unlabeled samples to remedy the insufficiency of labeled samples to train a supervised model is of great interest a

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An Entropy-Guided Adaptive Co-construction Method of State and Action Spaces in Reinforcement Learni adaptive and autonomous decentralized systems. In general, it is not easy to put RL into practical use. In previous research, Nagayoshi et al. have proposed an adaptive co-construction method of state and action spaces. However, the co-construction method needs two parameters for sufficiency of the

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Toroidal Approximate Identity Neural Networks Are Universal Approximators we investigate the universal approximation capability of one-hidden layer feedforward toroidal approximate identity neural networks. To this end, we present notions of toroidal convolution and toroidal approximate identity. Using these notions, we apply a convolution linear operator approach to pro

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Self-organizing Neural GroveGNN) are one of the most suitable base-classifiers for multiple classifier systems because of their simple settings and fast learning ability. However, the computation cost of the multiple classifier system based on SGNN increases in proportion to the numbers of SGNN. In this paper, we propose a nov

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Transfer Learning Using the Online FMM Modelrning leverages information from the source domain in solving problems in the target domain. Using the online FMM model, the data samples are trained one at a time. In order to evaluate the online FMM model, a transfer learning data set, based on data samples collected from real landmines, is used.