Use of pattern classification algorithms to interpret acoustic echolocation data from a walking-speed robotic sensor platform

2012 ◽  
Vol 132 (3) ◽  
pp. 1971-1971
Author(s):  
Eric A. Dieckman ◽  
Mark Hinders
Keyword(s):  
Pattern Classification ◽  
Walking Speed ◽  
Classification Algorithms ◽  
Sensor Platform ◽  
Robotic Sensor

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
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