Bayesian Ying-Yang System and Harmony Learning Theory

Major Readings

 

  • Lei Xu (2009), "Learning Algorithms for RBF Functions and Subspace Based Functions", Ch.3 in "Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods and Techniques", eds. by Olivas, Guerrero, Sober, Benedito, & Lopez, IGI Global publication, pp60-94.
  • Lei Xu (2009), "Machine learning problems from optimization perspective", A special issue for CDGO 07, Journal of Global Optimization, in press, DOI10.1007/s10898-008-9364-0.
  • Lei Xu (2008), `` Bayesian Ying Yang System, Best Harmony Learning, and Gaussian Manifold Based Family", In J.M. Zurada et al. (Eds.) Computational Intelligence: Research Frontiers, WCCI2008 Plenary/Invited Lectures, LNCS5050,  48–78, 2008.

·        Lei Xu (2007), Bayesian Ying Yang Learning,  In Scholarpedia, no.18395, http://scholarpedia.org, 2007.

  • Lei Xu (2007), `` A unified perspective  and new results on RHT computing, mixture based learning, and multi-learner based problem solving ",  Pattern Recognition,  (40) 2129–2153.
  • Lei Xu (2007), `` Trends on Regularization and Model Selection in Statistical Learning: A  Perspective from Bayesian Ying Yang Learning ", Studies in  Computational Intelligence,  63, pp. 365-406, 2007.
  • Lei Xu (2005), ``Fundamentals, Challenges, and Advances of Statistical Learning for Knowledge Discovery and Problem Solving: A BYY Harmony Perspective", Proceedings of International Conference on Neural Networks and Brain, Keynote talk, Vol. 1, pp. 24-55, Oct. 13-15,Beijing, China, 2005.
  • Lei Xu (2004), ``Temporal BYY Encoding, Markovian State Spaces, and Space Dimension Determination", IEEE Trans on Neural Networks, Vol. 15, No. 5, pp1276-1295, 2004.
  • Lei Xu (2004), ``Advances on BYY Harmony Learning: Information Theoretic Perspective, Generalized Projection Geometry, and Independent Factor Auto-determination", IEEE Trans on Neural Networks, Vol. 15, No. 4, pp885-902, 2004.
  • Lei Xu (2004), ``Bayesian Ying Yang Learning (I): A Unified Perspective for Statistical Modeling", Intelligent Technologies for Information Analysis, N. Zhong and J. Liu (eds), Springer, pp615-659, 2004.
  • Lei Xu (2004), ``Bayesian Ying Yang Learning (II): A New Mechanism for Model Selection and Regularization", Intelligent Technologies for Information Analysis, N. Zhong and J. Liu (eds), Springer, pp661-706, 2004.
  • Lei Xu (2003), `` BYY learning, regularized implementation, and model selection on modular networks with one hidden layer of binary units ", Neurocomputing, Vol. 51, pp 277-301, 2003. Errata to this paper is given here , which is published on Neurocomputing, Vol. 55, pp 405-406, 2003.
  • Lei Xu (2002a), `` Ying-Yang learning", The Handbook of Brain Theory and Neural Networks, 2nd ed., Michael A. Arbib, The MIT Press, pp1231-1237, 2002.
  • Lei Xu (2002b), ``BYY harmony learning, structural RPCL, and topological self-organizing on mixture models", Neural Networks, Vol. 15, pp1125-1151, 2002.
  • Lei Xu (2001a), ``BYY Harmony Learning, Independent State Space and Generalized APT Financial Analyses", IEEE Trans. on Neural Networks, Vol. 12, No.4, pp822-849, July, 2001. An Errata to this paper is given on IEEE Trans. on Neural Networks, Vol. 13, No.4, 1023, July, 2002.
  • Lei Xu (2001b), ``Best Harmony, Unified RPCL and Automated Model Selection for Unsupervised and Supervised Learning on Gaussian Mixtures, ME-RBF Models and Three-Layer Nets ", International Journal of Neural Systems, Vol.11, No.1, pp3-69, 2001.
  • Lei Xu (2000a), `` Temporal BYY Learning for State Space Approach, Hidden Markov Model and Blind Source Separation ", IEEE Trans on Signal Processing, Vol. 48, No. 7, 2132-2144, July, 2000.
  • Lei Xu (2000b), `` BYY Learning System and Theory for Parameter Estimation, Data Smoothing Based Regularization and Model Selection ", Neural, Parallel and Scientific Computations, Vol. 8, pp55-82, 2000.
  • Lei Xu (1998a), ``RBF Nets, Mixture Experts, and Bayesian Ying-Yang Learning", Neurocomputing, Vol. 19, No.1-3, pp223-257, 1998.
  • Lei Xu(1998b), ``Bayesian Kullback Ying-Yang Dependence Reduction Theory ", Neurocomputing, Vol.22, No.1-3, a special issue on Independence and artificial neural networks , pp81-112, 1998.
  • Lei Xu(1998c), ``Bayesian Ying-Yang Dimension Reduction and Determination", Journal of Computational Intelligence in Finance, Vol.6, No.5, a special issue on Complexity and Dimensionality Reduction in Finance . pp6-18, 1998.
  • Lei Xu (1997), ``Bayesian Ying-Yang Machine, Clustering and Number of Clusters", Pattern Recognition Letters, Vol.18, No.11-13, pp1167-1178, 1997.

Other Readings

·        An, Y.J., Hu, X.L.,  and Lei Xu (2006), ``A Comparative Investigation on Model Selection in Independent Factor Analysis" Journal of Mathematical Modeling and Algorithms 5,   pp.447–473.

·        X. Hu and Lei Xu (2004), ``A Comparative Study on Selection of Cluster Number and Local Subspace Dimension in the Mixture PCA Models", Advances in Neural Networks – ISNN 2006:  Lecture Notes in Computer Sciences, Vol. 3971, Editors:  Jun Wang, Zhang Yi, Jacek M. Zurada, Bao-Liang Lu, Hujun Yin,    pp. 1214 - 1221, Springer Verlag, 2006.

·        X. Hu and Lei Xu (2004), ``A comparative investigation on subspace dimension determination", Neural Networks , Vol. 17, pp1051¨C1059, 2004,

  • X. Hu and Lei Xu (2004), ``Investigation on Several Model Selection Criteria for Determining the Number of Cluster", Neural Information Processing - Letters and Reviews, Vol. 4, No. 1, pp1-10, July 2004,
  • Ma, J, Wang, T, and Lei Xu (2004), ``A gradient BYY harmony learning rule on Gaussian mixture with automated model selection", Neurocomputing, Vol 56, 481 - 487, 2004.
  • Yiu-ming Cheung and Lei Xu (2003), `` Further studies on temporal factor analysis: comparison and Kalman Filter-based algorithm ", Neurocomputing, Vol. 50, 2003, 87-103.
  • Ouyang Ning, Wing-kai Lam, K. Yamauchi and Lei Xu (1999), ``Using An Improved Back Propagation Learning Method to Diagnose The Sites of Cardiac Hypertrophy", MD Computing, Vol. 16, No.1, pp79-81, 1999.
  • Lei Xu (1997), ``New Advances on Bayesian Ying-Yang Learning System With Kullback and Non-Kullback Separation Functionals", Proceedings of 1997 IEEE-(INNS) Conference on Neural Networks, Houston, TX, June. 9-12, Vol. 3, pp1942-1947, 1997
  • Lei Xu (1997), `` Bayesian Ying-Yang System and Theory as A Unified Statistical Learning Approach (III): Models and Algorithms for Dependence Reduction, Data Dimension Reduction, ICA and Supervised Learning", Invited paper, K.W.Wong, I. King and D.Y.Yeung eds, Theoretical Aspects of Neural Computation: A Multidisciplinary Perspective (TANC97), Springer-Verlag, pp43-60, 1997.
  • Lei Xu (1997), ``Bayesian Ying-Yang System and Theory as A Unified Statistical Learning Approach: (II) From Unsupervised Learning to Supervised Learning and Temporal Modeling ", Invited paper, K.W.Wong, I. King and D.Y.Yeung eds, Theoretical Aspects of Neural Computation: A Multidisciplinary Perspective (TANC97), Springer-Verlag, pp25-42, 1997.
  • Lei Xu (1997), ``Bayesian Ying-Yang System and Theory: An Unified Approach for Statistical Learning: (I) Unsupervised and Semi-Unsupervised Learning", Invited paper, S. Amari and N. Kassabov eds., Brain-like Computing and Intelligent Information Systems, Springer-Verlag, pp241-274, 1997.
  • Lei Xu (1996),``A Unified Learning Scheme: Bayesian-Kullback YING-YANG Machine", Advances in Neural Information Processing Systems 8, eds., David S. Touretzky, Michael Mozer, Michael Hasselmo, MIT Press, Cambridge MA, 1996, pp444-450.
  • Lei Xu (1995), `` Bayesian-Kullback Coupled YING-YANG Machines: Unified Learnings and New Results on Vector Quantization", Proceedings of International Conference on Neural Information Processing, Keynote Speaker, Oct 30-Nov.3, Beijing, China, 1995, pp977-988.