Get Advances in Neuro-Information Processing: 15th International PDF

By Wei-Chen Cheng, Cheng-Yuan Liou (auth.), Mario Köppen, Nikola Kasabov, George Coghill (eds.)

ISBN-10: 3642030394

ISBN-13: 9783642030390

ISBN-10: 3642030408

ISBN-13: 9783642030406

The quantity set LNCS 5506 and LNCS 5507 constitutes the completely refereed post-conference lawsuits of the fifteenth foreign convention on Neural details Processing, ICONIP 2008, held in Auckland, New Zealand, in November 2008.

The 260 revised complete papers offered have been rigorously reviewed and chosen from various usual paper submissions and 15 precise equipped classes. 116 papers are released within the first quantity and 112 within the moment quantity. The contributions care for issues within the parts of knowledge mining tools for cybersecurity, computational versions and their functions to computer studying and trend reputation, lifelong incremental studying for clever structures, software of clever tools in ecological informatics, development attractiveness from real-world info by way of svm and different refined innovations, dynamics of neural networks, contemporary advances in brain-inspired applied sciences for robotics, neural info processing in cooperative multi-robot systems.

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Read Online or Download Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28, 2008, Revised Selected Papers, Part II PDF

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Extra info for Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28, 2008, Revised Selected Papers, Part II

Example text

The model is less susceptible to the presence of outliers and considers a Student-t distribution as assumption for errors on the mid-point of a learning data set. The assumption that the errors have a probability distribution grants to the model powerful possibilities of applying regular statistical hypothesis tests. 1 23 Symmetrical Linear Regression Models for Classical Data Suppose Y1 , . . , Yn as n independent random variables where density function is given by 1 (1) fYi (y) = √ g{(y − µi )2 /φ}, y ∈ IR, φ with µi ∈ IR and φ > 0 being the location and dispersion parameters, respec∞ tively.

Hirotaka Sakamoto, Yukiko Nakajima, Kazutoshi Sakakibara, Masahiro Ito, and Ikuko Nishikawa 953 962 970 978 986 XXXVI Table of Contents – Part I Practical Approach to Outlier Detection Using Support Vector Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . Junya Nishiguchi, Chosei Kaseda, Hirotaka Nakayama, Masao Arakawa, and Yeboon Yun 995 A Variant of Adaptive Mean Shift-Based Clustering . . . . . . . . . 1002 Fajie Li and Reinhard Klette XII Special Session: Neural Information Processing in Cooperative Multi-robot Systems Using Spiking Neural Networks for the Generation of Coordinated Action Sequences in Robots .

Moreover, this table shows that the average error rate decreases from the predefined interval [1, 10] to [1, 20]. It is why the number of regions in the predefined interval [1, 20] is four times the number of regions in the predefined interval [1, 10]. Table 1. 0260) To compare distances evaluated in this paper, Student’s t-test for independent samples at the significance level of 5% was used to determine whether there is a significant difference in error rate between L1 , L2 and L∞ distances. In Tables 3 and 4 µ1 , µ2 and µ3 are, respectively, the corresponding averages for L1 , L2 and L∞ distances.

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Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28, 2008, Revised Selected Papers, Part II by Wei-Chen Cheng, Cheng-Yuan Liou (auth.), Mario Köppen, Nikola Kasabov, George Coghill (eds.)


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