Statistical physics, mixtures of distributions, and the EM algorithm
Neural Computation
Adaptive mixtures of local experts
Neural Computation
Fast learning in networks of locally-tuned processing units
Neural Computation
Data Mining for Features Using Scale-Sensitive Gated Experts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Multiple Models Combination Method for Time Series Prediction
Journal of Intelligent and Robotic Systems
Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces
Machine Learning
On-line learning in changing environments with applications in supervised and unsupervised learning
Neural Networks - Computational models of neuromodulation
Multiple model-based reinforcement learning
Neural Computation
Analysis of Nonstationary Time Series Using Support Vector Machines
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Learning-Data Selection Mechanism through Neural Networks Ensemble
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
MOSAIC Model for Sensorimotor Learning and Control
Neural Computation
Applied Soft Computing
Time-Series Segmentation Using Predictive Modular Neural Networks
Neural Computation
Dynamic support vector machines for non-stationary time series forecasting
Intelligent Data Analysis
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We present a method for the unsupervised segmentation of data streams originating from different unknown sources that alternate in time. We use an architecture consisting of competing neural networks. Memory is included to resolve ambiguities of input-output relations. To obtain maximal specialization, the competition is adiabatically increased during training. Our method achieves almost perfect identification and segmentation in the case of switching chaotic dynamics where input manifolds overlap and input-output relations are ambiguous. Only a small dataset is needed for the training procedure. Applications to time series from complex systems demonstrate the potential relevance of our approach for time series analysis and short-term prediction.