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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.
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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