A pool of classifiers by SLP: A multi-class case
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 4142 LNCS, Page: 47-56
2006
- 4Citations
- 1Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Dynamics of training the group of single layer perceptrons aimed to solve multi-class pattern recognition problem is studied. It is shown that in special training of the perceptrons, one may obtain a pool of different classification algorithms. Means to improve training speed and reduce generalization error are studied. Training dynamics is illustrated by solving artificial multi-class pattern recognition task and important real world problem: detection of ten types of yeast infections from 1500 spectral features. © Springer-Verlag Berlin Heidelberg 2006.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=33749653150&origin=inward; http://dx.doi.org/10.1007/11867661_5; http://link.springer.com/10.1007/11867661_5; http://link.springer.com/content/pdf/10.1007/11867661_5.pdf; http://www.springerlink.com/index/10.1007/11867661_5; http://www.springerlink.com/index/pdf/10.1007/11867661_5; https://dx.doi.org/10.1007/11867661_5; https://link.springer.com/chapter/10.1007/11867661_5
Springer Science and Business Media LLC
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