Title :
Pattern recognition: neural networks in perspective
Author_Institution :
Dept. of Comput. & Inf. Sci., Ohio State Univ., Columbus, OH, USA
Abstract :
Invariant pattern recognition will be a problem facing neural networks for some time, and the challenge is to overcome the limitation of Hamming distance generalization. Four representative architectures that are able to generalize are reviewed. The architectures are the backpropagation network, the ART architecture, the dynamic link architecture, and associate memories. Image representation, segmentation, and invariance are discussed.<>
Keywords :
backpropagation; content-addressable storage; image recognition; neural nets; pattern recognition; ART architecture; Hamming distance generalization; adaptive resonance theory; associate memories; backpropagation network; dynamic link architecture; image representation; invariance; neural networks; pattern recognition; segmentation; Associative memory; Backpropagation; Equations; Hidden Markov models; Hopfield neural networks; Intelligent networks; Neural networks; Nonhomogeneous media; Pattern recognition; State estimation;
Journal_Title :
IEEE Expert
