|
|
|
|
| LEADER |
01000caa a22002652 4500 |
| 001 |
NLM177331968 |
| 003 |
DE-627 |
| 005 |
20250209042712.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231223s2001 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/3477.969492
|2 doi
|
| 028 |
5 |
2 |
|a pubmed25n0591.xml
|
| 035 |
|
|
|a (DE-627)NLM177331968
|
| 035 |
|
|
|a (NLM)18244854
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Laha, A
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Some novel classifiers designed using prototypes extracted by a new scheme based on self-organizing feature map
|
| 264 |
|
1 |
|c 2001
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Completed 02.10.2012
|
| 500 |
|
|
|a Date Revised 04.02.2008
|
| 500 |
|
|
|a published: Print
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a We propose two new comprehensive schemes for designing prototype-based classifiers. The scheme addresses all major issues (number of prototypes, generation of prototypes, and utilization of the prototypes) involved in the design of a prototype-based classifier. First we use Kohonen's self-organizing feature map (SOFM) algorithm to produce a minimum number (equal to the number of classes) of initial prototypes. Then we use a dynamic prototype generation and tuning algorithm (DYNAGEN) involving merging, splitting, deleting, and retraining of the prototypes to generate an adequate number of useful prototypes. These prototypes are used to design a "1 nearest multiple prototype (1-NMP)" classifier. Though the classifier performs quite well, it cannot reasonably deal with large variation of variance among the data from different classes. To overcome this deficiency we design a "1 most similar prototype (1-MSP)" classifier. We use the prototypes generated by the SOFM-based DYNAGEN algorithm and associate with each of them a zone of influence. A norm (Euclidean)-induced similarity measure is used for this. The prototypes and their zones of influence are fine-tuned by minimizing an error function. Both classifiers are trained and tested using several data sets, and a consistent improvement in performance of the latter over the former has been observed. We also compared our classifiers with some benchmark results available in the literature
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Pal, N R
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society
|d 1996
|g 31(2001), 6 vom: 15., Seite 881-90
|w (DE-627)NLM098252887
|x 1941-0492
|7 nnns
|
| 773 |
1 |
8 |
|g volume:31
|g year:2001
|g number:6
|g day:15
|g pages:881-90
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/3477.969492
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 31
|j 2001
|e 6
|b 15
|h 881-90
|