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Titlebook: Neural Information Processing; 18th International C Bao-Liang Lu,Liqing Zhang,James Kwok Conference proceedings 2011 Springer-Verlag GmbH B

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11#
發(fā)表于 2025-3-23 09:59:10 | 只看該作者
Network Flow Classification Based on the Rhythm of Packetslassification model to the different discretization data set of HTTP, EDONKEY, BITTORRENT, FTP and AIM. Experiment results show that our approach can achieve better precision and recall rate for these applications.
12#
發(fā)表于 2025-3-23 16:24:11 | 只看該作者
Energy-Based Feature Selection and Its Ensemble Versionion is introduced. Some experiments are conducted on the real world and synthesis data sets to demonstrate the ability of our feature selection algorithm and the stability improvement of the ensemble feature selection.
13#
發(fā)表于 2025-3-23 19:12:31 | 只看該作者
Emotiono: An Ontology with Rule-Based Reasoning for Emotion Recognitioned on their EEG data. We implement ‘Emotiono’ in Protégé 4.1 and evaluate its performance with EEG data gathered from the eNTERFACE06_EMOBRAIN Database. Using a 9-fold cross validation method for training and testing, ‘Emotiono’ reaches an average classification rate of 97.80% for recognizing 5 subjects’ emotional states.
14#
發(fā)表于 2025-3-24 00:12:43 | 只看該作者
The Self-Organizing Map Tree (SOMT) for Nonlinear Data Causality Predictiona and to observe the prediction processes. Nonlinear data relationships and possible prediction outcomes are inspected through the processes of the SOMT that shows a good predictability of the target output for the given inputs.
15#
發(fā)表于 2025-3-24 05:03:52 | 只看該作者
16#
發(fā)表于 2025-3-24 08:47:57 | 只看該作者
Conference proceedings 2011sing, ICONIP 2011, held in Shanghai, China, in November 2011. .The 262 regular session papers presented were carefully reviewed and selected from numerous submissions. .The papers of part I are organized in topical sections on perception, emotion and development, bioinformatics, biologically inspire
17#
發(fā)表于 2025-3-24 13:59:23 | 只看該作者
18#
發(fā)表于 2025-3-24 16:18:45 | 只看該作者
An Infinite Mixture of Inverted Dirichlet Distributionsore the performance of the proposed approach on the challenging problem of text categorization. The results show that the proposed approach is effective for positive data modeling when compared to those reported using infinite Gaussian mixture.
19#
發(fā)表于 2025-3-24 21:21:46 | 只看該作者
20#
發(fā)表于 2025-3-24 23:58:31 | 只看該作者
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