Hierarchical neural networks for text categorization (poster abstract)
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchical Text Categorization Using Neural Networks
Information Retrieval
Online adaptive decision trees
Neural Computation
A recursive MISD architecture for pattern matching
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Multi-Classifier Systems: Review and a roadmap for developers
International Journal of Hybrid Intelligent Systems
On-line multi-modal speaker diarization
Proceedings of the 9th international conference on Multimodal interfaces
Balancing the Role of Priors in Multi-Observer Segmentation Evaluation
Journal of Signal Processing Systems
An analysis of generalization error in relevant subtask learning
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Layered architecture for real time sign recognition: Hand gesture and movement
Engineering Applications of Artificial Intelligence
Learning a discriminative classifier using shape context distances
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Perceptually grounded self-diagnosis and self-repair of domain knowledge
Knowledge-Based Systems
Improving the expert networks of a modular multi-net system for pattern recognition
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
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We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM''s). Learning is treated as a maximum likelihood problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the parameters of the architecture. We also develop an on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain.