As an example of using the heuristic, the simple genetic algorithm is compared with a set of hillclimbers on a simple subset of the hyperplane-defined functions, the pothole functions. Several methods have been proposed where typically we construct a multi-class classifier by combining several binary classifiers. Some authors also proposed methods that consider all classes of data at once. Especially for methods solving multi-class SVM in one step, a much larger optimization problem is required so up to now experiments are limited to small data sets. Results also show that for large problems the method by considering all data at once in general needs fewer support vectors.
Any comments are very welcome.
Best Chih-Jen Lin Dept. of Computer Science National Taiwan Univ. We show the advantages of using SVMs with kernels of infinite complexity on noisy target rules, which, in contrast to common theoretical beliefs, are found to achieve optimal generalization error although the training error does not converge to the generalization error. Moreover, we find a universal asymptotics of the learning curves which only depend on the target rule but not on the SVM kernel. Physical Review Letters, in press. We develop a procedure for computing the full temporal entropy and information of ensembles of neural spike trains, which performs reliably for limited samples of data.