Vector quantization and signal compression
Vector quantization and signal compression
A study of support vectors on model independent example selection
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Invariance of neighborhood relation under input space to feature space mapping
Pattern Recognition Letters
Fast pattern selection for support vector classifiers
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Inferring problem solving strategies using eye-tracking: system description and evaluation
Proceedings of the 10th Koli Calling International Conference on Computing Education Research
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Support Vector Machines (SVM) have been applied successfully in a wide variety of fields in the last decade. The SVM problem is formulated as a convex objective function subject to box constraints that needs to be maximized, a quadratic programming (QP) problem. In order to solve the QP problem on larger data sets specialized algorithms and heuristics are required. In this paper we present a new data-squashing method for selecting training instances in support vector learning. Inspired by the growing neural gas algorithm and learning vector quantization we introduce a new, parameter robust neural gas variant to retrieve an initial approximation of the training set containing only those samples that will likely become support vectors in the final classifier. This first approximation is refined in the border areas, defined by neighboring neurons of different classes, yielding the final training set. We evaluate our approach on synthetic as well as real-life datasets, comparing run-time complexity and accuracy to a random sampling approach and the exact solution of the support vector machine. Results show that runtime-complexity can be significantly reduced while achieving the same accuracy as the exact solution and that furthermore our approach does not not rely on data set specific parameterization of the sampling rate like random sampling for doing so. Source code, binary executables as well as the reformatted standard data sets are available for download at http://www.know-center.tugraz.at/forschung/ knowledge_relationship_discovery/downloads_demos/ sngsvm_source_executables