Batch map extensions of the kernel-based maximum entropy learning rule

Batch map extensions of the kernel-based maximum entropy learning rule

In this letter, two batch-map extensions are described for the kernel-based maximum entropy learning rule (kMER). In the first, the weights are iteratively set to weighted component-wise medians, while in the second the generalized median is used, enabling kMER to process symbolic data. Simulations...

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Veröffentlicht in:IEEE transaction on neural networks and learning systems 2006-03, Vol.17 (2), p.529-532
Hauptverfasser: Gautama, T., Van Hulle, M.M.
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Applied sciences
Artificial Intelligence
Batch map
Computer science
control theory
systems
Computer Simulation
Connectionism. Neural networks
Cooling
Decision Support Techniques
Educational programs
Entropy
Exact sciences and technology
generalized median
Image Interpretation, Computer-Assisted - methods
Iterative algorithms
kMER
Learning
Maximum entropy
Models, Theoretical
Neural networks
Neural Networks (Computer)
Neurons
Numerical Analysis, Computer-Assisted
Pattern Recognition, Automated - methods
Psychology
Temperature control
Terminology
Training data
Vector quantization
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Beschreibung
Zusammenfassung:In this letter, two batch-map extensions are described for the kernel-based maximum entropy learning rule (kMER). In the first, the weights are iteratively set to weighted component-wise medians, while in the second the generalized median is used, enabling kMER to process symbolic data. Simulations are performed to illustrate the extensions.
ISSN:1045-9227
2162-237X
1941-0093
2162-2388
DOI:10.1109/TNN.2006.871722