Classification using a sparse combination of basis functions

Kovács Kornél and Kocsor András: Classification using a sparse combination of basis functions. In: Acta cybernetica, (17) 2. pp. 311-323. (2005)

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Combinations of basis functions are applied here to generate and solve a convex reformulation of several well-known machine learning algorithms like certain variants of boosting methods and Support Vector Machines. We call such a reformulation a Convex Networks (CN) approach. The nonlinear Gauss-Seidel iteration process for solving the CN problem converges globally and fast as we prove. A major property of CN solution is the sparsity, the number of basis functions with nonzero coefficients. The sparsity of the method can effectively be controlled by heuristics where our techniques are inspired by the methods from linear algebra. Numerical results and comparisons demonstrate the effectiveness of the proposed methods on publicly available datasets. As a consequence, the CN approach can perform learning tasks using far fewer basis functions and generate sparse solutions.

Item Type: Article
Journal or Publication Title: Acta cybernetica
Date: 2005
Volume: 17
Number: 2
ISSN: 0324-721X
Page Range: pp. 311-323
Language: English
Place of Publication: Szeged
Event Title: Conference for PhD Students in Computer Science (4.) (2004) (Szeged)
Related URLs:
Uncontrolled Keywords: Számítástechnika, Kibernetika
Additional Information: Bibliogr.: p. 322-323. ; összefoglalás angol nyelven
Subjects: 01. Natural sciences
01. Natural sciences > 01.02. Computer and information sciences
Date Deposited: 2016. Oct. 15. 12:25
Last Modified: 2022. Jun. 15. 12:23

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