By Günter Hommel; Huanye Sheng
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Extra info for Human interaction with machines : proceedings of the 6th international workshop held at the Shanghai Jiao Tong University, March 15-16, 2005
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8] L. Zhang, A. Cichocki, and S. Amari. Self-adaptive blind source separation based on activation function adaptation. IEEE Transactions on Neural Networks, 15(2):233-244, 2004. -R. Müller , M. Krauledat , G. Dornhege , S. Jähnichen , G. Curio1 , and B. IDA, Kekuléstr. 7, 12 489 Berlin, Germany 2 University of Potsdam, August-Bebel-Str. 89, 14 482 Potsdam, Germany 3 Technical 4 University Berlin, Str. d. 17. Juni 135, 10 623 Berlin, Germany Dept. of Neurology, Campus Benjamin Franklin, Charité University Medicine Berlin, Hindenburgdamm 30, 12 203 Berlin, Germany Summary.
Experiments with a new boosting algorithm. In Proceedings of 13th International Conference on Machine Learning, 1996, 148-156 David D. Lewis, Yiming Yang, Tony G. Rose, Fan Li, “RCV1: a new benchmark collection for text categorization research”, Journal of Machine Learning Research 5(2004) 361-397 Thorsten Joachims, Learning to classify text using support vector machine:method, theory, and algorithms. Kluwer Academic Publishers, 2002. de Summary. Bluetooth wireless technology has gained in importance in recent years and can now be found in a multitude of devices ranging from keyboards, mice, and printers to handhelds, laptops, and mobile phones, to name only a few.
Learning algorithms for the demixing model and temporal structures are also provided. 1 Problem Formulation Assume that si , i = 1,2 ," , n are n mutually stochastically independent source signals, of which each is temporally correlated and zero mean. ). For the sake of simplicity, we use the notation N Ai ( z ) = 1 − ¦ a ip z − p . p =1 where z is the z-transform variable. (2) Multi-Modal Machine Attention If source signals are known, the coefficients of dictor Ai (z) can be obtained by minimizing the least squares sense.
