کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
531194 869818 2006 16 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Data representations and generalization error in kernel based learning machines
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Data representations and generalization error in kernel based learning machines
چکیده انگلیسی

This paper focuses on the problem of how data representation influences the generalization error of kernel based learning machines like support vector machines (SVM) for classification. Frame theory provides a well founded mathematical framework for representing data in many different ways. We analyze the effects of sparse and dense data representations on the generalization error of such learning machines measured by using leave-one-out error given a finite amount of training data. We show that, in the case of sparse data representations, the generalization error of an SVM trained by using polynomial or Gaussian kernel functions is equal to the one of a linear SVM. This is equivalent to saying that the capacity of separating points of functions belonging to hypothesis spaces induced by polynomial or Gaussian kernel functions reduces to the capacity of a separating hyperplane in the input space. Moreover, we show that, in general, sparse data representations increase or leave unchanged the generalization error of kernel based methods. Dense data representations, on the contrary, reduce the generalization error in the case of very large frames. We use two different schemes for representing data in overcomplete systems of Haar and Gabor functions, and measure SVM generalization error on benchmarked data sets.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 39, Issue 9, September 2006, Pages 1588–1603
نویسندگان
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