Integer partition problem: Theoretical approach to improving accuracy of classifier ensembles / Khachay M., Pobery M., Khachay D. // International Journal of Artificial Intelligence. - 2015. - V. 13, l. 1. - P. 135-146.

ISSN:
09740635
Type:
Article
Abstract:
New results (along with rigorous proofs) confirming the connection between the principle of structural risk minimization and theoretical combinatorics are presented. In particular, a new special subclass of the well known Integer Partition Problem is introduced. Close relation of this subclass to pruning procedures of ensemble classifiers is proved. © 2015 by IJAI (CESER PUBLICATIONS).
Author keywords:
Computational learning theory; Ensemble classifiers; Pruning
Index keywords:
нет данных
DOI:
нет данных
Смотреть в Scopus:
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84924357655&partnerID=40&md5=50914829e343e80f0365bc3c9b7fbb17
Соавторы в МНС:
Другие поля
Поле Значение
Link https://www.scopus.com/inward/record.uri?eid=2-s2.0-84924357655&partnerID=40&md5=50914829e343e80f0365bc3c9b7fbb17
Affiliations Department of Mathematical Programming, Krasovsky Institute of Mathematics and Mechanics, 16 S.Kovalevskoy str, Ekaterinburg, Russian Federation; Department of Mathematics and Computer Science, Ural Federal University, 19 Mira str, Ekaterinburg, Russian Federation; Department of Radioelectronics, Omsk State Technical University, 11 Mira ave, Omsk, Russian Federation
Author Keywords Computational learning theory; Ensemble classifiers; Pruning
References Andrews, G.E., (1976) The Theory of Partitions, , Vol. 2 of Encyclopedia of Mathematics and its Applications, Addison-Wesley Publishing Co., Reading, MA-London-Amsterdam. Reprinted by Cambridge University Press, Cambridge, 1998; Bartlett, P.L., The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network (2006) IEEE Trans. Inf. Theor, 44 (2), pp. 525-536; Caruana, R., Niculescu-Mizil, A., Crew, G., Ksikes, A., Ensemble selection from libraries of models, In Proceedings of the 21st International Conference on Machine Learning (2004) ACM Press, pp. 137-144; Freund, Y., Boosting a weak learning algorithm by majority (1995) Information and Computation, 121, pp. 256-285; Gopalakrishnan, V., Ramaswamy, C., Sentiment learning from imbalanced dataset: An ensemble based method (2014) International Journal of Artificial Intelligence, 12 (2), pp. 75-87; Han, H., Qiao, J., An efficient algorithm for feedforward neural network reconstructing and its application (2011) International Journal of Artificial Intelligence, 7 (A11), pp. 142-150; Hardy, G.H., Ramanujan, S., Asymptotic formula in combinatory analysis (1918) Proceedings of the London Mathematical Society, S2-17 (1), pp. 75-115. , http://plms.oxfordjournals.org/content/s2-17/1/75.short; Hardy, G.H., Wright, E.M., (1979) An Introduction to the Theory of Numbers, , Oxford Science Publications, Clarendon Press, Oxford; Khachai, M., On one combinatorial problem concerned with the notion of minimal committee (2001) Pattern Recognition and Image Analysis, 11 (1), pp. 45-46; Khachai, M., Computational complexity of the minimum committee problem and related problems (2006) Doklady Mathematics, 73, pp. 138-141; Khachai, M., Mazurov, V., Rybin, A., Committee constructions for solving problems of selection, diagnostics, and prediction, Proc (2002) Steklov Institute Math, (1), pp. SS67-S101; Khachay, M., Estimate of the number of members in the minimal committee of a system of linear inequalities (1997) Computational Mathematics and Mathematical Physics, 37 (11), pp. 1356-1361; Khachay, M., Poberii, M., Complexity and approximability of committee polyhedral separability of sets in general position (2009) Informatica, 20 (2), pp. 217-234; Kim, Y., Street, N., Menczer, F., Meta-evolutionary ensembles (2002) In IEEE International Joint Conference on Neural Networks, IEEE, pp. 2791-2796; Margineantu, D.D., Dietterich, T.G., (1997) Pruning Adaptive Boosting, ICML’97, pp. 211-218. , Morgan Kaufmann Publishers Inc; Prodromidis, A.L., Chan, P.K., Stolfo, S.J., (2000) Meta-Learning in Distributed Data Mining Systems, , Issues and Approaches, AAAI Press; Schapire, R.E., Freund, Y., Bartlett, P., Lee, W.S., Boosting the margin: A new explanation for the effectiveness of voting methods, Proceedings of the Fourteenth International Conference on Machine Learning, ICML’97 (1997) Morgan Kaufmann Publishers Inc., pp. 322-330; Schapire, R., Freund, Y., (2012) Boosting: Foundations and Algorithms, , MIT Press; Schölkopf, B., Smola, A., (2002) Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, , MIT press; Sra, S., Nowozin, S., Wright, S., (2012) Optimization for Machine Learning, , Neural information processing series, MIT Press; Valiant, L.G., A theory of the learnable (1984) Communications of the ACM, 27 (11), pp. 1134-1142; Vapnik, V., (1998) Statistical Learning Theory, , Wiley; Zhao, Z.-Y., Xie, W.-F., Hong, H., Hybrid optimization method of evolutionary parallel gradient search (2010) International Journal of Artificial Intelligence, 5 (A10), pp. 1-16
Publisher CESER Publications
Language of Original Document English
Abbreviated Source Title Int. J. Artif. Intell.
Source Scopus