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dc.contributor.authorKopco, Norberten_US
dc.contributor.authorCarpenter, Gailen_US
dc.date.accessioned2017-07-28T02:16:41Z
dc.date.available2017-07-28T02:16:41Z
dc.date.issued2002-12
dc.identifier.urihttps://hdl.handle.net/2144/23147
dc.descriptionAlso published in the International Journal of Hybrid Intelligent Systems, Volume 1, January, 2004en_US
dc.description.abstractA memory-based learning system called PointMap is a simple and computationally efficient extension of Condensed Nearest Neighbor that allows the user to limit the number of exemplars stored during incremental learning. PointMap evaluates the information value of coding nodes during training, and uses this index to prune uninformative nodes either on-line or after training. These pruning methods allow the user to control both a priori code size and sensitivity to detail in the training data, as well as to determine the code size necessary for accurate performance on a given data set. Coding and pruning computations are local in space, with only the nearest coded neighbor available for comparison with the input; and in time, with only the current input available during coding. Pruning helps solve common problems of traditional memory-based learning systems: large memory requirements, their accompanying slow on-line computations, and sensitivity to noise. PointMap copes with the curse of dimensionality by considering multiple nearest neighbors during testing without increasing the complexity of the training process or the stored code. The performance of PointMap is compared to that of a group of sixteen nearest-neighbor systems on benchmark problems.en_US
dc.description.sponsorshipThis research was supported by grants from the Air Force Office of Scientific Research (AFOSR F49620-98-l-0108, F49620-0l-l-0397, and F49620-0l-l-0423) and the Office of Naval Research (ONR N00014-0l-l-0624).en_US
dc.language.isoen_US
dc.publisherBoston University Center for Adaptive Systems and Department of Cognitive and Neural Systemsen_US
dc.relation.ispartofseriesBU CAS/CNS Technical Reports;CAS/CNS-TR-2002-012
dc.rightsCopyright 2002 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and / or special permission.en_US
dc.subjectMemory-based learningen_US
dc.subjectNearest neighboren_US
dc.subjectOn-line pruningen_US
dc.subjectPost-training pruningen_US
dc.subjectIncremental learningen_US
dc.subjectPointMapen_US
dc.titlePointMap: A real-time memory-based learning system with on-line and post-training pruningen_US
dc.typeTechnical Reporten_US
dc.rights.holderBoston University Trusteesen_US


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