On generalization bounds, projection profile, and margin distribution
نویسندگان
چکیده
We study generalization properties of linear learning algorithms and develop a data dependent approach that is used to derive generalization bounds that depend on the margin distribution. Our method makes use of random projection techniques to allow the use of existing VC dimension bounds in the effective, lower, dimension of the data. Comparisons with existing generalization bound show that our bounds are tighter and meaningful in cases existing bounds are not.
منابع مشابه
Generalization Bounds for Linear Learning Algorithms
We study generalization properties of linear learning algorithms and develop a data dependent approach that is used to derive generalization bounds that depend on the margin distribution. Our method makes use of random projection techniques to allow the use of existing VC dimension bounds in the effective, lower, dimension of the data. Comparisons with existing generalization bound show that ou...
متن کاملRobust Bounds on Generalization from the Margin Distribution
A number of results have bounded generalization of a classi er in terms of its margin on the training points There has been some debate about whether the minimum margin is the best measure of the distribution of training set margin values with which to estimate the generalization Fre und and Schapire have shown how a di erent function of the margin distribution can be used to bound the number o...
متن کاملA Note on the Generalization Performance of Kernel Classifiers with Margin
We present distribution independent bounds on the generalization misclassification performance of a family of kernel classifiers with margin. Support Vector Machine classifiers (SVM) stem out of this class of machines. The bounds are derived through computations of the Vγ dimension of a family of loss functions where the SVM one belongs to. Bounds that use functions of margin distributions (i.e...
متن کامل2 Margin Distribution and Soft Margin
Typical bounds on generalization of Support Vector Machines are based on the minimum distance between training examples and the separating hyperplane. There has been some debate as to whether a more robust function of the margin distribution could provide generalization bounds. Freund and Schapire (1998) have shown how a diierent function of the margin distribution can be used to bound the numb...
متن کاملEmpirical Margin Distributions and Bounding the Generalization Error of Combined Classifiers
We prove new probabilistic upper bounds on generalization error of complex classifiers that are combinations of simple classifiers. Such combinations could be implemented by neural networks or by voting methods of combining the classifiers, such as boosting and bagging. The bounds are in terms of the empirical distribution of the margin of the combined classifier. They are based on the methods ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2002