Committee polyhedral separability: complexity and polynomial approximation
| LEADER | caa a22 4500 | ||
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| 001 | 605477825 | ||
| 003 | CHVBK | ||
| 005 | 20210128100402.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20151001xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s10994-015-5505-0 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10994-015-5505-0 | ||
| 100 | 1 | |a Khachay |D Michael |u Krasovsky Institute of Mathematics and Mechanics UB RAS, Yekaterinburg, Russia |4 aut | |
| 245 | 1 | 0 | |a Committee polyhedral separability: complexity and polynomial approximation |h [Elektronische Daten] |c [Michael Khachay] |
| 520 | 3 | |a We consider the minimum affine separating committee (MASC) combinatorial optimization problem, which is related to ensemble machine learning techniques on the class of linear weak classifiers combined by the rule of simple majority. Actually, the MASC problem is a mathematical formalization of the famous Vapnik-Chervonenkis principle of structural risk minimization in the mentioned class of classifiers. According to this principle, it is required to construct a best performance ensemble classifier belonging to a family of the least possible VC-dimension. It is known that the MASC problem is NP-hard and remains intractable in spaces of any fixed dimension $$n>1$$ n > 1 even under an additional constraint on the separated sets to be in general position. This special case of the MASC problem called MASC-GP(n) is the main subject of interest of the present paper. To design polynomial-time approximation algorithms for a class of combinatorial optimization problems containing the MASC problem, we propose a new framework, adjusting the well-known Multiple Weights Update method. Following this approach, we construct polynomial-time approximation algorithms with state-of-the-art approximation guarantee for the MASC-GP(n) problem. The results obtained provide a theoretical framework for learning a high-performance ensembles of affine classifiers. | |
| 540 | |a The Author(s), 2015 | ||
| 690 | 7 | |a Polyhedral separability |2 nationallicence | |
| 690 | 7 | |a Affine committees |2 nationallicence | |
| 690 | 7 | |a Computational complexity |2 nationallicence | |
| 690 | 7 | |a Approximability |2 nationallicence | |
| 773 | 0 | |t Machine Learning |d Springer US; http://www.springer-ny.com |g 101/1-3(2015-10-01), 231-251 |x 0885-6125 |q 101:1-3<231 |1 2015 |2 101 |o 10994 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10994-015-5505-0 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10994-015-5505-0 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 100 |E 1- |a Khachay |D Michael |u Krasovsky Institute of Mathematics and Mechanics UB RAS, Yekaterinburg, Russia |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Machine Learning |d Springer US; http://www.springer-ny.com |g 101/1-3(2015-10-01), 231-251 |x 0885-6125 |q 101:1-3<231 |1 2015 |2 101 |o 10994 | ||