Abstract
Bayesian Ying-Yang (BYY) harmony learning has provided a new learning mechanism to implement automated model selection on finite mixture during parameter learning with a set of sample data. In this paper, two kinds of BYY harmony learning algorithms, called the batch-way gradient learning algorithm and the simulated annealing learning algorithm, respectively, are proposed for the Weibull mixture modeling based on the maximization of the harmony function on the two different architectures of the BYY learning system related to Weibull mixture such that model selection can be made automatically during the parameter learning on Weibull mixture. The two proposed algorithms are both demonstrated well by the simulation experiments on some typical sample data sets with certain degree of overlap.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Akaike, H.: A New Look at the Statistical Model Identification. IEEE Trans. Automatic Control, AC- 19, 716–723 (1974)
Bozdogan, H.: Model Selection and Akaike’s Information Criterion: the General Theory and its Analytical Extensions. Psychometrika 52, 345–370 (1978)
Scharz, G.: Estimating the Dimension of a Model. The Annals of Statistics 6, 461–464 (1978)
Xu, L.: Ying-Yang Machine: a Bayesian-Kullback Scheme for Unified Learnings and New Results on Vector Quantization. In: Proceedings of the 1995 International Conference on Neural Information Processing (ICONIP 1995), vol. 2, pp. 977–988 (1995)
Xu, L.: Best Harmony, Unified RPCL and Automated Model Selection for Unsupervised and Supervised Learning on Gaussian Mixtures, Three-layer Nets and ME-RBF-SVM Models. International Journal of Neural Systems 11, 43–69 (2001)
Xu, L.: Ying-Yang Learning. In: Arbib, M.A. (ed.) The Handbook of Brain Theory and Neural Networks, 2nd edn., pp. 1231–1237. The MIT Press, Cambridge (2002)
Xu, L.: BYY Harmony Learning, Structural RPCL, and Topological Self-organizing on Mixture Models. Neural Networks 15, 1231–1237 (2002)
Ma, J., Wang, T., Xu, L.: A Gradient BYY Harmony Learning Rule on Gaussian Mixture with Automated Model Selection. Neurocomputing 56, 481–487 (2004)
Ma, J., Gao, B., Wang, Y., et al.: Conjugate and Natural Gradient Rules for BYY Harmony Learning on Gaussian Mixture with Automated Model Selection. International Journal of Pattern Recognition and Artificial Intellegence 19, 701–713 (2005)
Ma, J., Wang, L.: BYY Harmony Learning on Finite Mixture: Adaptive Gradient Implementation and a Floating RPCL Mechanism. Neural Processing Lett. 24(1), 19–40 (2006)
Ma, J., He, X.: A Fast Fixed-point BYY Harmony Learning Algorithm on Gaussian Mixture with Automated Model Selection. Pattern Recognition Letters 29(6), 701–711 (2008)
Ma, J., Liu, J.: The BYY Annealing Learning Algorithm for Gaussian Mixture with Automated Model Selection. Pattern Recognition 40, 2029–2037 (2007)
Liu, J., Ma, J.: An Adaptive Gradient BYY Learning Rule for Poisson Mixture with Automated Model Selection. In: Huang, D.-S., Heutte, L., Loog, M. (eds.) ICIC 2007. LNCS, vol. 4681, pp. 1059–1069. Springer, Heidelberg (2007)
Robert, B.A.: The New Weibull Handbook, 4th edn. North Palm Beach, Fla. (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ren, Z., Ma, J. (2008). BYY Harmony Learning on Weibull Mixture with Automated Model Selection. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_66
Download citation
DOI: https://doi.org/10.1007/978-3-540-87732-5_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87731-8
Online ISBN: 978-3-540-87732-5
eBook Packages: Computer ScienceComputer Science (R0)