Taxonomy of Pattern Classification Algorithms

2001 ◽  
pp. 27-75
Author(s):  
Šarūnas Raudys
Keyword(s):  
Pattern Classification ◽  
Classification Algorithms

Making an Electronic Nose Versatile

2011 ◽  
pp. 78-101
Author(s):  
Nabarun Bhattacharyya ◽  
Bipan Tudu ◽  
Rajib Bandyopadhyay
Keyword(s):  
Pattern Classification ◽  
Electronic Nose ◽  
Incremental Learning ◽  
Learning Algorithms ◽  
Classification Performance ◽  
Classification Algorithms ◽  
Machine Olfaction ◽  
Entire Dataset

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.

Toxicant Identification by a Luminescent Bacterial Bioreporter Panel: Application of Pattern Classification Algorithms

2008 ◽  
Vol 42 (22) ◽  
pp. 8486-8491 ◽  
Author(s):  
Tal Elad ◽  
Etay Benovich ◽  
Sagi Magrisso ◽  
Shimshon Belkin
Keyword(s):  
Pattern Classification ◽  
Classification Algorithms

scholarly journals On pattern classification algorithms introduction and survey

1968 ◽  
Vol 56 (12) ◽  
pp. 2101-2114 ◽  
Author(s):  
Yu-Chi Ho ◽  
A.K. Agrawala
Keyword(s):  
Pattern Classification ◽  
Classification Algorithms

Spectral and Topographic Microstructure of Brain Alpha Activity During Drowsiness at Sleep Onset and REM Sleep

2000 ◽  
Vol 14 (3) ◽  
pp. 151-158 ◽  
Author(s):  
José Luis Cantero ◽  
Mercedes Atienza
Keyword(s):  
Rem Sleep ◽  
Spectral Component ◽  
Sleep Onset ◽  
Quantitative Information ◽  
Maximum Energy ◽  
Alpha Activity ◽  
The Other ◽  
Classification Algorithms ◽  
Microstructural Properties ◽  
Brain States

Abstract High-resolution frequency methods were used to describe the spectral and topographic microstructure of human spontaneous alpha activity in the drowsiness (DR) period at sleep onset and during REM sleep. Electroencephalographic (EEG), electrooculographic (EOG), and electromyographic (EMG) measurements were obtained during sleep in 10 healthy volunteer subjects. Spectral microstructure of alpha activity during DR showed a significant maximum power with respect to REM-alpha bursts for the components in the 9.7-10.9 Hz range, whereas REM-alpha bursts reached their maximum statistical differentiation from the sleep onset alpha activity at the components between 7.8 and 8.6 Hz. Furthermore, the maximum energy over occipital regions appeared in a different spectral component in each brain activation state, namely, 10.1 Hz in drowsiness and 8.6 Hz in REM sleep. These results provide quantitative information for differentiating the drowsiness alpha activity and REM-alpha by studying their microstructural properties. On the other hand, these data suggest that the spectral microstructure of alpha activity during sleep onset and REM sleep could be a useful index to implement in automatic classification algorithms in order to improve the differentiation between the two brain states.

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