Titlebook: Artificial Neural Networks in Pattern Recognition; 4th IAPR TC3 Worksho Friedhelm Schwenker,Neamat Gayar Conference proceedings 2010 Spring

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antecedence

https://doi.org/10.1007/978-3-662-33000-5alues of intensity and employing top-down hierarchical rule-based classifiers, we can develop accurate human-interpretable AU-to-expression converters. Extensive experiments on Cohn-Kanade database show the superiority of the proposed method, in comparison with support vector machines, hidden Markov models, and neural network classifiers.

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Dungeon

Ergebnisse der Tunneluntersuchungen,novel feature weighting scheme is also presented by employing Particle Swarm Optimization (PSO) for the weights calculation. The new method, called PSOFLDA, is tested on real MES datasets and compared with other techniques to prove its superiority.

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Evaluation of Feature Selection by Multiclass Kernel Discriminant Analysisments using benchmark datasets, we show that the KDA criterion has performance comparable with that of the selection criterion based on the SVM-based recognition rate with cross-validation and can reduce computational cost. We also show that the KDA criterion can terminate feature selection stably using cross-validation as a stopping condition.

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