Titlebook: Artificial Neural Networks and Machine Learning - ICANN 2011; 21st International C Timo Honkela,W?odzis?aw Duch,Samuel Kaski Conference pro

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https://doi.org/10.1007/978-3-662-31589-7ifier we can additionally apply the Particle Swarm Optimization algorithm to tune its free parameters. Our experimental results show that by applying Particle Swarm Optimization on the Sub-class Linear Discriminant Error Correcting Output Codes framework we get a significant improvement in the classification performance.

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Optimizing Linear Discriminant Error Correcting Output Codes Using Particle Swarm Optimization,ifier we can additionally apply the Particle Swarm Optimization algorithm to tune its free parameters. Our experimental results show that by applying Particle Swarm Optimization on the Sub-class Linear Discriminant Error Correcting Output Codes framework we get a significant improvement in the classification performance.

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Fermat’s Last Theorem for Amateursn provides a fast adjustment of the BCI system to mild changes of the signal. The proposed algorithm was validated on artificial and real data sets. In comparison to generic Multi-Way PLS, the recursive algorithm demonstrates good performance and robustness.

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Fermat’s Last Theorem for Amateursed for regression problems of big and complex datasets. It was applied to the problem of steel temperature prediction in the electric arc furnace in order to decrease the process duration at one of the steelworks.

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Weakly Supervised Learning of Foreground-Background Segmentation Using Masked RBMs,very weak supervision. The model generates plausible samples and performs foreground-background segmentation. We demonstrate that representing foreground objects independently of the background can be beneficial in recognition tasks.