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Soft Computing
Published in: Soft Computing 4/2013

01-04-2013 | Focus

Dynamical memory control based on projection technique for online regression

Authors: Hui Jiang, Bo Zhang

Published in: Soft Computing | Issue 4/2013

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Abstract

In this paper, a dynamical memory control strategy based on projection technique is proposed for kernel-based online regression. Namely, when an instance is removed from the memory, its contribution will be kept by projecting the regression function onto the subspace expanded instead of throwing it away cheaply. This strategy is composed of incremental and decremental controls. To the former, a new example will be added to the memory if it brings a significant change to the regression function, otherwise discarded by the projection technique. The latter is applied when a new instance is added to the memory, or the memory size has reached a predefined budget. The proposed method is analyzed theoretically and its performance is tested on four benchmark data sets.
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Literature
Bi T, Zhang B, Xu R (2011) Dynamics of intraday serial correlation in China’s stock market. Comm Stat-Simul Comput 40(10):1637–1650MathSciNetMATHCrossRef
Cesa-Bianchi N, Conconi A, Gentile C (2006) Tracking the best hyperplane with a simple budget perceptron. In: Proceedings of the 19th annual conference on computational learning theory, vol 4005. Springer, LNCS, Berlin, pp 483–496
Crammer K, Kandola J, Singer Y (2003) Online classification on a budget. In: Thrun T, Saul L, Schölkopf B (eds) Advances in neural information processing systems, vol 16. MIT Press, Cambridge, pp 225–232
Crammer K, Dekel O, Keshet J, Shalev-Shwartz S, Singer Y (2006) Online passive–aggressive algorithms. J Mach Learn Res 7:551–585MathSciNetMATH
Csató L, Opper M (2000) Sparse representation for Gaussian process models. In: Leen TK, Dietterich TG, Tresp V (eds) Advances in neural information processing systems, vol 13. MIT Press, Cambridge, pp 444–450
Cucker F, Zhou DX (2007) Learning theory: an approximation theory viewpoint. Cambridge University Press, New York
Dekel O, Shalev-Shwartz S, Singer Y (2007) The Forgetron: a kernel-based perceptron on a budget. SIAM J Comput 37(5):1342–1372MathSciNetCrossRef
Downs T, Gates KE, Masters A (2001) Exact simplification of support vectors solutions. J Mach Learn Res 2:293–297
Engel Y, Mannor S, Meir R (2004) The kernel recursive least-squares algorithm. IEEE Trans. Signal Process 52(8):2275–2285MathSciNetCrossRef
Ferrari S, Bellocchio F, Piuri V, Alberto Borghese N (2010) A hierarchical RBF online learning algorithm for real-time 3-D scanner. IEEE Trans Neural Netw 21(2):275–285CrossRef
Freund Y, Schapire RE (1999) Large margin classification using the Perceptron algorithm. Mach Learn 37(3):277–296MATHCrossRef
He W (2008) Forecasting electricity load with optimized local learning models. Int J Elec Power Energy Syst 30(10):603–608CrossRef
He W, Wu S (2012) A kernel-based perceptron with dynamic memory. Neural Netw 25(1):106–113CrossRef
Karasuyama M, Takeuchi I (2010) Multiple incremental decremental learning of support vector machines. IEEE Trans Neural Netw 21(7):1048–1059CrossRef
Kivinen J, Smola AJ, Williamson RC (2004) Online learning with kernels. IEEE Trans Signal Process 52(8):2165-2176
Liang NY, Huang GB, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans. Neural Netw. 17(6):1411–1423CrossRef
Orabona F, Keshet J, Caputo B (2009) Bounded kernel-based online learning. J Mach Learn Res 10:2643–2666MathSciNetMATH
Ozawa S, Roy A, Roussinov D (2009) A multitask learning model for online pattern recognition. IEEE Trans Neural Netw 20(3):430–445CrossRef
Schölkopf B, Herbrich R, Smola AJ (2001) A generalized representer theorem. In: Proceedings of the 14th annual conference on computational learning theory, vol 2111. Springer, LNCS, Berlin, pp 416–426
Shalev-Shwartz S, Singer Y (2007) A primal–dual perspective of online learning algorithms. Mach Learn 69(2–3):115–142CrossRef
Shalev-Shwartz S, Singer Y, Srebro N (2007) Pegasos: primal estimated sub-gradient solver for svm. In: Proceedings of the 24th international conference on machine learning, Corvalis, pp 807–814
Wang Z, Crammer K, Vucetic S (2010) Multi-class pegasos on a budget. In: Proceedings of the 27th international conference machine learning, Haifa, pp 1143–1150
Xu J, Kannan D, Zhang B (2001) Optimal dynamic control for the defined benefit pension plans with stochastic benefit outgo. Stoch Anal Appl 25(1):201–236MathSciNetCrossRef
Xu Z, Zhang R, Jing W (2009) When does online BP training converge? IEEE Trans Neural Netw 20(10):1529–1539CrossRef
Yang H, Xu Z, King I, Lyu M (2010) Online learning for group Lasso. In: Proceedings of the 27th international conference on machine learning, Haifa, pp 1191–1198
Zhang K, Kwok JT (2010) Clustered Nystrom method for large scale manifold learning and dimension reduction. IEEE Trans Neural Netw 21(10):1576–1587
Zhang H, Wu W, Liu F, Yao M (2009) Boundedness and convergence of online gradient method with penalty for feedforward neural networks. IEEE Trans Neural Netw 20(6):1050–1054CrossRef
Metadata
Title
Dynamical memory control based on projection technique for online regression
Authors
Hui Jiang
Bo Zhang
Publication date
01-04-2013
Publisher
Springer-Verlag
Published in
Soft Computing / Issue 4/2013
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-012-0929-y

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