Many applications of machine learning involve sparse high-dimensional data, where the number of input features is (much) larger than the number of data samples, d≫n. Predictive modeling of such data is very ill-posed and prone to overfitting. Several recent studies for modeling high-dimensional data employ new learning methodology called Learning through Contradictions or Universum Learning due to Vapnik (1998,2006). This method incorporates a priori knowledge about application data, in the form of additional Universum samples, into the learning process. This paper investigates generalization properties of the Universum-SVM and how they are related to characteristics of the data. We describe practical conditions for evaluating the effectiveness of Random Averaging Universum. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Cherkassky, V., & Dai, W. (2009). Empirical study of the universum SVM learning for high-dimensional data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5768 LNCS, pp. 932–941). https://doi.org/10.1007/978-3-642-04274-4_96
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