Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Classification with Sums of Separable Functions

: Garcke, Jochen

Postprint urn:nbn:de:0011-n-5126626 (190 KByte PDF)
MD5 Fingerprint: 42e60bb4bfdb921afa936a5f59f0dd22
The original publication is available at
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: 978-3-642-15880-3
ISBN: 3-642-15879-X
ISBN: 978-3-642-15879-7
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) <2010, Barcelona>
Conference Paper, Electronic Publication
Fraunhofer SCAI ()
Classification; Sums of Separable Functions; Machine Learning

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.