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A Unified Approach to Feature Selection and Learning in Unsupervised Environments
September 1975 (vol. 24 no. 9)
pp. 948-952
A.L. Lakshminarasimhan, School of Automation, Indian Institute of Science
Here the twin problems of feature selection and learning are tackled simultaneously to obtain a unified approach to the problem of pattern recognition in an unsupervised environment. This is achieved by combining a feature selection scheme based on the stochastic learning automata model with an unsupervised learning scheme such as learning with a probabilistic teacher. Test implementation of this scheme using the remotely sensed agricultural data of the Purdue laboratory for agricultural remote sensing (LARS) in a simulated unsupervised mode, has brought out the efficacy of this integrated system of feature selection and learning.
Index Terms:
Pattern recognition in unsupervised environment, stochastic automata model for feature selection, unified feature selection and learning.
Citation:
A.L. Lakshminarasimhan, B.V. Dasarathy, "A Unified Approach to Feature Selection and Learning in Unsupervised Environments," IEEE Transactions on Computers, vol. 24, no. 9, pp. 948-952, Sept. 1975, doi:10.1109/T-C.1975.224346
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