Recursive Bayesian estimation using piece-wise constant approximations
Automatica (Journal of IFAC)
Training multilayer perceptrons with the extended Kalman algorithm
Advances in neural information processing systems 1
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Computation
Exact adaptive filters for Markov chains observed in Gaussian noise
Automatica (Journal of IFAC)
Issues in Bayesian analysis of neural network models
Neural Computation
Regularisation in sequential learning algorithms
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Robust Full Bayesian Learning for Radial Basis Networks
Neural Computation
A graphical model for evolutionary optimization
Evolutionary Computation
Visual Tracking Using Particle Filters with Gaussian Process Regression
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Gaussian sum approach with optimal experiment design for neural network
SIP '07 Proceedings of the Ninth IASTED International Conference on Signal and Image Processing
A one-step unscented particle filter for nonlinear dynamical systems
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Nonlinear identification based on diagonal recurrent neural network and particle filter
ICNC'09 Proceedings of the 5th international conference on Natural computation
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
A new learning algorithm for diagonal recurrent neural network
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Support vector machine adaptive control of nonlinear systems
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part II
Sequential support vector machine control of nonlinear systems by state feedback
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Stochastic volatility modeling with computational intelligence particle filters
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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We discuss a novel strategy for training neural networks using sequential Monte Carlo algorithms and propose a new hybrid gradient descent / sampling importance resampling algorithm (HySIR). In terms of computational time and accuracy, the hybrid SIR is a clear improvement over conventional sequential Monte Carlo techniques. The new algorithm may be viewed as a global optimization strategy that allows us to learn the probability distributions of the network weights and outputs in a sequential framework. It is well suited to applications involving on-line, nonlinear, and nongaussian signal processing. We show how the new algorithm outperforms extended Kalman filter training on several problems. In particular, we address the problem of pricing option contracts, traded in financial markets. In this context, we are able to estimate the one-step-ahead probability density functions of the options prices.