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Erschienen in:
Improving Automatic Classifiers Through Interaction Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

On the Cesàro-Means-Based Orthogonal Series Approach to Learning Time-Varying Regression Functions

verfasst von : Piotr Duda, Lena Pietruczuk, Maciej Jaworski, Adam Krzyzak

Erschienen in: Artificial Intelligence and Soft Computing

Verlag: Springer International Publishing

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Abstract

In this paper an incremental procedure for nonparametric learning of time-varying regression function is presented. The procedure is based on the Cesàro-means of orthogonal series. Its tracking properties are investigated and convergence in probability is shown. Numerical simulations are performed using the Fejer’s kernels of the Fourier orthogonal series.

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Vorheriges Kapitel Complexity of Rule Sets Induced from Data Sets with Many Lost and Attribute-Concept Values
Nächstes Kapitel Nonparametric Estimation of Edge Values of Regression Functions
Literatur
1.
Abbas, J.: The bipolar choquet integrals based on ternary-element sets. J. Artif. Intell. Soft Comput. Res. 6(1), 13–21 (2016)CrossRef
2.
Aghdam, M.H., Heidari, S.: Feature selection using particle swarm optimization in text categorization. J. Artif. Intell. Soft Comput. Res. 4(4), 231–238 (2015)
3.
Bas, E.: The training of multiplicative neuron model based artificial neural networks with differential evolution algorithm for forecasting. J. Artif. Intell. Soft Comput. Res. 6(1), 5–11 (2016)CrossRef
4.
Bilski, J., Smolag, J.: Parallel architectures for learning the RTRN and Elman dynamic neural networks. IEEE Trans. Parallel Distrib. Syst. 26(9), 2561–2570 (2015)CrossRef
5.
Bilski, J., Smoląg, J., Żurada, J.M.: Parallel approach to the Levenberg-Marquardt learning algorithm for feedforward neural networks. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) Artificial Intelligence and Soft Computing. LNCS, vol. 9119, pp. 3–14. Springer, Heidelberg (2015)CrossRef
6.
Bilski, J., Smoląg, J., Galushkin, A.I.: The parallel approach to the conjugate gradient learning algorithm for the feedforward neural networks. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014, Part I. LNCS, vol. 8467, pp. 12–21. Springer, Heidelberg (2014)CrossRef
7.
Bose, R., van der Aalst, W., Zliobaite, I., Pechenizkiy, M.: Dealing with concept drifts in process mining. IEEE Trans. Neural Netw. Learn. Syst. 25(1), 154–171 (2014)CrossRef
8.
Chu, J.L., Krzyżak, A.: The recognition of partially occluded objects with support vector machines, convolutional neural networks and deep belief networks. J. Artif. Intell. Soft Comput. Res. 4(1), 5–19 (2014)CrossRef
9.
Cpalka, K., Rebrova, O., Nowicki, R., et al.: On design of flexible neuro-fuzzy systems for nonlinear modelling. Int. J. Gen. Syst. 42(6), 706–720 (2013). Special Issue: SICrossRefMATH
10.
Cpalka, K., Zalasinski, M., Rutkowski, L.: New method for the on-line signature verification based on horizontal partitioning. Pattern Recogn. 47(8), 2652–2661 (2014)CrossRef
11.
Cpalka, K., Rutkowski, L.: Flexible Takagi-Sugeno fuzzy systems. In: Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), vol. 1–5, pp. 1764–1769 (2005)
12.
Ditzler, G., et al.: Learning in nonstationary environments: a survey. Comput. Intell. Mag. IEEE 10(4), 12–25 (2015)MathSciNetCrossRef
13.
Duda, P., Jaworski, M., Pietruczuk, L.: On pre-processing algorithms for data stream. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part II. LNCS, vol. 7268, pp. 56–63. Springer, Heidelberg (2012)CrossRef
14.
Elwell, R., Polikar, R.: Incremental learning of concept drift in nonstationary environments. IEEE Trans. Neural Netw. 22(10), 1517–1531 (2011)CrossRef
15.
Galkowski, T., Rutkowski, L.: Nonparametric recovery of multivariate functions with applications to system identification. Proc. IEEE 73, 942–943 (1985). New YorkCrossRef
16.
Galkowski, T., Rutkowski, L.: Nonparametric fitting of multivariable functions. IEEE Trans. Autom. Control AC–31, 785–787 (1986)CrossRefMATH
17.
Galkowski, T.: Nonparametric estimation of boundary values of functions. Arch. Control Sci. 3(1–2), 85–93 (1994)MathSciNetMATH
18.
Gama, J., Fernandes, R., Rocha, R.: Decision trees for mining data streams. Intell. Data Anal. 10(1), 23–45 (2006)
19.
Jaworski, M., Duda, P., Pietruczuk, L.: On fuzzy clustering of data streams with concept drift. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part II. LNCS, vol. 7268, pp. 82–91. Springer, Heidelberg (2012)CrossRef
20.
Jaworski, M., Er, M.J., Pietruczuk, L.: On the application of the parzen-type kernel regression neural network and order statistics for learning in a non-stationary environment. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part I. LNCS, vol. 7267, pp. 90–98. Springer, Heidelberg (2012)CrossRef
21.
Jaworski, M., Pietruczuk, L., Duda, P.: On resources optimization in fuzzy clustering of data streams. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part II. LNCS, vol. 7268, pp. 92–99. Springer, Heidelberg (2012)CrossRef
22.
Kitajima, R., Kamimura, R.: Accumulative information enhancement in the self-organizing maps and its application to the analysis of mission statements. J. Artif. Intell. Soft Comput. Res. 5(3), 161–176 (2015)CrossRef
23.
Knop, M., Cierniak, R., Shah, N.: Video compression algorithm based on neural network structures. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014, Part I. LNCS, vol. 8467, pp. 715–724. Springer, Heidelberg (2014)CrossRef
24.
Krzyzak, A., Pawlak, M.: Distribution-free consistency of a nonparametric kernel regression estimate and classification. IEEE Trans. Inf. Theor. 30(1), 78–81 (1984)MathSciNetCrossRefMATH
25.
Krzyzak, A.: The rates of convergence of kernel regression estimates and classification rules. IEEE Trans. Inf. Theor. 32(5), 668–679 (1986)MathSciNetCrossRefMATH
26.
Korytkowski, M., Rutkowski, L., Scherer, R.: Fast image classification by boosting fuzzy classifiers. Inf. Sci. 327, 175–182 (2016)MathSciNetCrossRef
27.
Laskowski, Ł.: A novel hybrid-maximum neural network in stereo-matching process. Neural Comput. Appl. 23(7–8), 2435–2450 (2013)CrossRef
28.
Laskowski, Ł., Jelonkiewicz, J., Hayashi, Y.: Extensions of hopfield neural networks for solving of stereo-matching problem. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) Artificial Intelligence and Soft Computing. LNCS, vol. 9119, pp. 59–71. Springer, Heidelberg (2015)CrossRef
29.
Laskowski, Ł., Laskowska, M., Jelonkiewicz, J., Boullanger, A.: Molecular approach to hopfield neural network. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) Artificial Intelligence and Soft Computing. LNCS, vol. 9119, pp. 72–78. Springer, Heidelberg (2015)CrossRef
30.
Miyajima, H., Shigei, N.: Performance comparison of hybrid electromagnetism-like mechanism algorithms with descent method. J. Artif. Intell. Soft Comput. Res. 5(4), 271–282 (2015)CrossRef
31.
Mleczko, W., Kapuscinski, T., Nowicki, R.: Rough deep belief network - Application to incomplete handwritten digits pattern classification. In: Dregvaite, G., Damasevicius, R. (eds.) ICIST 2015. Communications in Computer and Information Science, pp. 400–411. Springer, Switzerland (2015)
32.
Nowak, B.A., Nowicki, R.K., Starczewski, J.T., Marvuglia, A.: The learning of neuro-fuzzy classifier with fuzzy rough sets for imprecise datasets. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014, Part I. LNCS, vol. 8467, pp. 256–266. Springer, Heidelberg (2014)CrossRef
33.
Nowicki, R.: Rough sets in the neuro-fuzzy architectures based on non-monotonic fuzzy implications. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 518–525. Springer, Heidelberg (2004)CrossRef
34.
Nowicki, R., Rutkowski, L.: Soft techniques for bayesian classification. In: Rutkowski, L., Kacprzyk, J. (eds.) Neural Networks and Soft Computing. Advances in Soft Computing, pp. 537–544. Springer, Heidelberg (2003)CrossRef
35.
Pietruczuk, L., Duda, P., Jaworski, M.: A new fuzzy classifier for data streams. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part I. LNCS, vol. 7267, pp. 318–324. Springer, Heidelberg (2012)CrossRef
36.
Pietruczuk, L., Zurada, J.M.: Weak convergence of the recursive parzen-type probabilistic neural network in a non-stationary environment. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds.) PPAM 2011, Part I. LNCS, vol. 7203, pp. 521–529. Springer, Heidelberg (2012)CrossRef
37.
Pietruczuk, L., Duda, P., Jaworski, M.: Adaptation of decision trees for handling concept drift. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part I. LNCS, vol. 7894, pp. 459–473. Springer, Heidelberg (2013)CrossRef
38.
Rutkowska, D., Nowicki, R., Rutkowski, L.: Neuro-fuzzy architectures with various implication operators. In: Sincák, P., Vašcák, J., Kvasnička, V., Mesiar, R. (eds.) State of the Art in Computational Intelligence. Advances in soft Computing, pp. 214–219. Springer, Heidelberg (2000)CrossRef
39.
Rutkowski, L.: Sequential estimates of probability densities by orthogonal series and their application in pattern classification. IEEE Trans. Syst. Man Cybern. SMC–10(12), 918–920 (1980)MathSciNetMATH
40.
Rutkowski, L.: Sequential estimates of a regression function by orthogonal series with applications in discrimination. In: Révész, P., Schmetterer, L., Zolotarev, V.M. (eds.) The First Pannonian Symposium on Mathematical Statistics. LNCS, pp. 236–244. Springer, New York (1981)CrossRef
41.
Rutkowski, L.: On Bayes risk consistent pattern recognition procedures in a quasi-stationary environment. IEEE Trans. Pattern Anal. Mach. Intell. PAMI–4(1), 84–87 (1982)MathSciNetCrossRefMATH
42.
Rutkowski, L.: On-line identification of time-varying systems by nonparametric techniques. IEEE Trans. Automatic Control AC–27, 228–230 (1982)CrossRefMATH
43.
Rutkowski, L.: On nonparametric identification with prediction of time-varying systems. IEEE Trans. Autom. Control AC–29, 58–60 (1984)CrossRefMATH
44.
45.
Rutkowski, L.: The real-time identification of time-varying systems by nonparametric algorithms based on the Parzen kernels Int. J. Syst. Sci. 16, 1123–1130 (1985). LondonCrossRefMATH
46.
Rutkowski, L.: An application of multiple Fourier series to identification of multivariable nonstationary systems. Int. J. Syst. Sci. 20(10), 1993–2002 (1989)MathSciNetCrossRefMATH
47.
Rutkowski, L.: Nonparametric learning algorithms in the time-varying environments. Sig. Process. 18, 129–137 (1989)MathSciNetCrossRef
48.
Rutkowski, L.: Adaptive probabilistic neural-networks for pattern classification in time-varying environment. IEEE Trans. Neural Netw. 15, 811–827 (2004)CrossRef
49.
Rutkowski, L.: Generalized regression neural networks in time-varying environment. IEEE Trans. Neural Netw. 15, 576–596 (2004)CrossRef
50.
Rutkowski, L., Cpalka, K.: Compromise approach to neuro-fuzzy systems. In: Intelligent Technologies - Theory and Applications: New Trends in Intelligent Technologies. Frontiers in Artificial Intelligence and Applications, vol. 76 pp. 85–90 (2002)
51.
Rutkowski, L., Pietruczuk, L., Duda, P., Jaworski, M.: Decision trees for mining data streams based on the McDiarmid’s bound. IEEE Trans. Knowl. Data Eng. 25(6), 1272–1279 (2013)CrossRef
52.
Rutkowski, L., Jaworski, M., Duda, P., Pietruczuk, L.: Decision trees for mining data streams based on the gaussian approximation. IEEE Trans. Knowl. Data Eng. 26(1), 108–119 (2014)CrossRef
53.
Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: The CART decision trees for mining data streams. Inf. Sci. 266, 1–15 (2014)CrossRef
54.
Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: A new method for data stream mining based on the misclassification error. IEEE Trans. Neural Netw. Learn. Syst. 26(5), 1048–1059 (2015)MathSciNetCrossRef
55.
Sakurai, S., Nishizawa, M.: A new approach for discovering top-K sequential patterns based on the variety of items. J. Artif. Intell. Soft Comput. Res. 5(2), 141–153 (2015)CrossRef
56.
Sansone, G.: Orthogonal Functions, Pure and Applied Mathematics. Interscience Publishers, Inc., New York (1959)
57.
Serdah, A., Ashour, W.: Clustering large-scale data based on modified affinity propagation algorithm. J. Artif. Intell. Soft Comput. Res. 6(1), 23–33 (2016)CrossRef
58.
59.
Szegö, G.: Orthogonal Polynomials, vol. 23. American Mathematical Society Coll. Publ., Providence (1959)MATH
60.
Woźniak, M., Kempa, W.M., Gabryel, M., Nowicki, R.K., Shao, Z.: On applying evolutionary computation methods to optimization of vacation cycle costs in finite-buffer queue. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014, Part I. LNCS, vol. 8467, pp. 480–491. Springer, Heidelberg (2014)CrossRef
61.
Ye, Y., Squartini, S., Piazza, F.: Online sequential extreme learning machine in nonstationary environments. Neurocomputing 116, 94–101 (2013)CrossRef
62.
Zliobaite, I., Bifet, A., Pfahringer, B., Holmes, G.: Active learning with drifting streaming data. IEEE Trans. Neural Netw. Learn. Syst. 25(1), 27–39 (2014)CrossRef
Metadaten
Titel
On the Cesàro-Means-Based Orthogonal Series Approach to Learning Time-Varying Regression Functions
verfasst von
Piotr Duda
Lena Pietruczuk
Maciej Jaworski
Adam Krzyzak
Copyright-Jahr
2016
Buch
Artificial Intelligence and Soft Computing

Print ISBN: 978-3-319-39383-4

Electronic ISBN: 978-3-319-39384-1

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-39384-1_4