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- W2480288723 abstract "Because of these factors, it is necessary to make the system flexible in such a way that the system is able to update an existing classifier without affecting the classification performance on old data, and such classifiers should have the property as being both stable and plastic. Conventional pattern classification algorithms require the entire dataset during training, and thereby fail to meet the criteria of being plastic and stable at the same time. The incremental learning algorithms possess these features, and thus, the electronic nose systems become extremely versatile when equipped with these classifiers. In this chapter, the authors describe different incremental learning algorithms for machine olfaction." @default.
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- W2480288723 date "2011-05-25" @default.
- W2480288723 modified "2023-09-27" @default.
- W2480288723 title "Making an Electronic Nose Versatile" @default.
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- W2480288723 doi "https://doi.org/10.4018/978-1-61520-915-6.ch003" @default.
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