On the Extraction of Pattern Features from Imperfectly Identified Samples
IEEE Transactions on Computers
An image content description technique for the inspection of specular objects
EURASIP Journal on Advances in Signal Processing
| Hi-index | 14.98 |
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.