G. Ratsch, T. Onoda and K.-R. Muller, \Soft margins for adaboost," Tech. Rep. NC-TR-1998-021, Royal Holloway College, University of London, UK, 1998, Submitted to Machine Learning. Home/Search Document Not in Database Summary Related Articles Check |
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Fisher Discriminant Analysis With Kernels - Mika, Rätsch, Weston.. (1999) (65 citations) (Correct)
....of the data set, do not separate the two classes well (although higher order Kernel PCA features might be discriminating, too) To evaluate the performance of our new approach we performed an extensive comparison to other state of the art classi ers. The experimental setup was chosen in analogy to [10] and we compared the Kernel Fisher Discriminant to AdaBoost, regularized AdaBoost (also [10] and Support Vector Machines (with Gaussian kernel) For KFD we used Gaussian kernels, too, and the regularized within class scatter from (11) After the optimal direction w 2 F was found, we computed ....
....might be discriminating, too) To evaluate the performance of our new approach we performed an extensive comparison to other state of the art classi ers. The experimental setup was chosen in analogy to [10] and we compared the Kernel Fisher Discriminant to AdaBoost, regularized AdaBoost (also [10]) and Support Vector Machines (with Gaussian kernel) For KFD we used Gaussian kernels, too, and the regularized within class scatter from (11) After the optimal direction w 2 F was found, we computed projections onto it by using (10) To estimate an optimal threshold on the extracted feature, ....
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G. Ratsch, T. Onoda and K.-R. Muller, \Soft margins for adaboost," Tech. Rep. NC-TR-1998-021, Royal Holloway College, University of London, UK, 1998, Submitted to Machine Learning.
Fisher Discriminant Analysis with Kernels - Mika, Rätsch, Weston.. (1999) (65 citations) Self-citation (Atsch Uller London) (Correct)
No context found.
G. Ratsch, T. Onoda and K.-R. Muller, \Soft margins for adaboost," Tech. Rep. NC-TR-1998-021, Royal Holloway College, University of London, UK, 1998, Submitted to Machine Learning.
Discriminative Densities from Maximum Contrast Estimation - Meinicke, Twellmann (2002) (Correct)
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G. Ratsch, T. Onoda, and K.-R. Muller. Soft margins for AdaBoost. Technical Report NC-TR1998 -021, Department of Computer Science, Royal Holloway, University of London, Egham, UK, August 1998. Submitted to Machine Learning.
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