Image analysis – Learning systems – Neural networks
Reexamination Certificate
2011-08-02
2011-08-02
Mehta, Bhavesh M (Department: 2624)
Image analysis
Learning systems
Neural networks
C706S015000
Reexamination Certificate
active
07991223
ABSTRACT:
A Neural Gas network used for pattern recognition, sequence and image processing is extended to a supervised classifier with labeled prototypes by extending a cost function of the Neural Gas network with additive terms, each of which increases with a difference between elements of the class labels of a prototype and a training data point and decreases with their distance. The extended cost function is then iteratively minimized by adapting weight vectors of the prototypes. The trained network can then be used to classify mass spectrometric data, especially mass spectrometric data derived from biological samples.
REFERENCES:
Martinetz, Thomas; “Neural-Gas” Network for Vector Quantization and its Application to Time-Series Prediction; IEEE Transactions on Neural Networks, vol. 4, No. 4, pp. 558-569, Jul. 1993.
Villman, et al.; “Fuzzy Labeled Neural Gas for Fuzzy Classification”; Proceedings of WSOM 2005, Paris, France, pp. 283-290, Sep. 5-8, 2005.
Villman, et al.; “Supervised Neural Gas and Relevance Learning in Learning Vector Quantization” 4th Workshop on Self-Organizing Maps, Kitakyushu (Japan) 2003, pp. 47-52.
Villmann, et al.; “Supervised Neural Gas for Learning Vector Quantization”; 5th German Workshop on Artificial Life, IOS Press, pp. 9-18, 2002.
Villman, et al.; “Fuzzy Classification by Fuzzy Labeled Neural Gas”; Proceedings of WSOM 2005, Paris, France.
Schleif, et al.; “Analysis and Visualization of Proteomic Data by Fuzzy Labeled Self-Organizing Maps”; 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06) pp. 919-924.
Hammer Barbara
Schleif Frank-Michael
Villmann Thomas
Bruker Daltonik GmbH
Law Offices of Paul E. Kudirka
Mehta Bhavesh M
Shah Utpal
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