Titlebook: Intelligent Data Engineering and Automated Learning - IDEAL 2007; 8th International Co Hujun Yin,Peter Tino,Xin Yao Conference proceedings

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Variational GTM data. It was originally formulated as a constrained mixture of distributions, for which the adaptive parameters were determined by Maximum Likelihood (ML), using the Expectation-Maximization (EM) algorithm. In this paper, we define an alternative variational formulation of GTM that provides a full

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Exploration of a Text Collection and Identification of Topics by Clustering006 is presented. The topics were identified by selecting from the abstracts belonging to each cluster the terms with the highest scores using different ranking schemes. The ranking scheme based on log-entropy showed better performance in this task than other more classical TFIDF schemes. An evaluat

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Asynchronous BCI Control of a Robot Simulator with Supervised Online Trainingous BCI offers more natural human-machine interaction, but it is a great challenge to train and adapt an asynchronous BCI online because the user’s control intention and timing are usually unknown. This paper proposes a novel motor imagery based asynchronous BCI for controlling a simulated robot in

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语源学

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