Title :
Pattern classification using neural networks
Author_Institution :
MIT Lincoln Lab., Lexington, MA, USA
Abstract :
The author extends a previous review and focuses on feed-forward neural-net classifiers for static patterns with continuous-valued inputs. He provides a taxonomy of neural-net classifiers, examining probabilistic, hyperplane, kernel, and exemplar classifiers. He then discusses back-propagation and decision-tree classifiers; matching classifier complexity to training data; GMDH (generalized method of data handling) networks and high-order nets; K nearest-neighbor classifiers; the feature-map classifier; the learning vector quantizer; hypersphere classifiers; and radial-basis function classifiers.<>
Keywords :
neural nets; pattern recognition; K nearest-neighbor classifiers; back-propagation; classifier complexity; continuous-valued inputs; decision-tree; exemplar; feature-map classifier; feed-forward; generalized method of data handling; high-order nets; hyperplane; hypersphere classifiers; kernel; learning vector quantizer; neural networks; neural-net; probabilistic; radial-basis function classifiers; static patterns; training data; Data handling; Feedforward systems; Kernel; Neural networks; Pattern classification; Taxonomy; Training data;
Journal_Title :
Communications Magazine, IEEE
