Analysis of switching dynamics with competing support vector machines

We present a framework for the unsupervised segmentation of switching dynamics using support vector machines. Following the architecture by Pawelzik et al., where annealed competing neural networks were used to segment a nonstationary time series, in this paper, we exploit the use of support vector machines, a well-known learning technique. First, a new formulation of support vector regression is proposed. Second, an expectation-maximization step is suggested to adaptively adjust the annealing parameter. Results indicate that the proposed approach is promising.

[1]  L. Glass,et al.  Oscillation and chaos in physiological control systems. , 1977, Science.

[2]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[3]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[4]  Rose,et al.  Statistical mechanics and phase transitions in clustering. , 1990, Physical review letters.

[5]  K. Pawelzik,et al.  Optimal Embeddings of Chaotic Attractors from Topological Considerations , 1991 .

[6]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[7]  Simon Haykin,et al.  Classification of radar clutter using neural networks , 1991, IEEE Trans. Neural Networks.

[8]  Steven J. Nowlan,et al.  Mixtures of Controllers for Jump Linear and Non-Linear Plants , 1993, NIPS.

[9]  R.J. Hathaway,et al.  Switching regression models and fuzzy clustering , 1993, IEEE Trans. Fuzzy Syst..

[10]  Andreas S. Weigend,et al.  Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .

[11]  Klaus Pawelzik,et al.  Detecting Coherence in Neuronal Data , 1994 .

[12]  Klaus-Robert Müller,et al.  Analysis of switching dynamics with competing neural networks , 1995 .

[13]  Klaus-Robert Müller,et al.  Annealed Competition of Experts for a Segmentation and Classification of Switching Dynamics , 1996, Neural Computation.

[14]  Andreas S. Weigend,et al.  Taking time seriously: hidden Markov experts applied to financial engineering , 1997, Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr).

[15]  Roderick Murray-Smith,et al.  Multiple Model Approaches to Modelling and Control , 1997 .

[16]  Athanasios Kehagias,et al.  Time-Series Segmentation Using Predictive Modular Neural Networks , 1997, Neural Computation.

[17]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  Bernhard Schölkopf,et al.  Comparing support vector machines with Gaussian kernels to radial basis function classifiers , 1997, IEEE Trans. Signal Process..

[19]  Gunnar Rätsch,et al.  Predicting Time Series with Support Vector Machines , 1997, ICANN.

[20]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[21]  Klaus Pawelzik,et al.  Hidden Markov mixtures of experts with an application to EEG recordings from sleep , 1999 .

[22]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[23]  Tomaso A. Poggio,et al.  Regularization Networks and Support Vector Machines , 2000, Adv. Comput. Math..

[24]  Klaus-Robert Müller,et al.  Identification of nonstationary dynamics in physiological recordings , 2000, Biological Cybernetics.

[25]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[26]  Simon Haykin,et al.  An Approach to Adaptive Classification , 2001 .

[27]  Chih-Jen Lin,et al.  Training ν-Support Vector Classifiers: Theory and Algorithms , 2001 .

[28]  Chih-Jen Lin,et al.  Training v-Support Vector Classifiers: Theory and Algorithms , 2001, Neural Computation.