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Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Classification with Sums of Separable Functions
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Postprint urn:nbn:de:0011n5126626 (190 KByte PDF) MD5 Fingerprint: 42e60bb4bfdb921afa936a5f59f0dd22 The original publication is available at springerlink.com Created on: 2.10.2018 
 Balcázar, J.L.: Machine learning and knowledge discovery in databases. European conference, ECML PKDD 2010 : Barcelona, Spain, September 20  24, 2010 ; proceedings, part I Berlin: Springer, 2010 (Lecture Notes in Computer Science 6321) ISBN: 9783642158803 ISBN: 364215879X ISBN: 9783642158797 pp.458473 
 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) <2010, Barcelona> 

 English 
 Conference Paper, Electronic Publication 
 Fraunhofer SCAI () 
 Classification; Sums of Separable Functions; Machine Learning 
Abstract
We present a novel approach for classification using a discretised function representation which is independent of the data locations. We construct the classifier as a sum of separable functions, extending the paradigm of separated representations. Such a representation can also be viewed as a low rank tensor product approximation. The central learning algorithm is linear in both the number of data points and the number of variables, and thus is suitable for large data sets in high dimensions. We show that our method achieves competitive results on several benchmark data sets which gives evidence for the utility of these representations.