DocumentCode :
2707358
Title :
A support vector hierarchical method for multi-class classification and rejection
Author :
Wang, Yu-Chiang Frank ; Casasent, David
Author_Institution :
Dept Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA
fYear :
2009
fDate :
14-19 June 2009
Firstpage :
3281
Lastpage :
3288
Abstract :
We address both recognition of true classes and rejection of unseen false classes inputs, as occurs in many realistic pattern recognition problems. we advance a hierarchical binary-decision classifier and produce analog outputs at each node, with yields a new soft-decision hierarchical is designed by our new support vector clustering method, which selects the classes to be separated at each node in the hierarchy. Use of our SVRDM (support vector representation and discrimination machine) classifiers at each node provides generalization and rejection ability. The soft-decision SVRDM output allows use of the confidence score for each class at each node; this is shown to improve classification (for true classes) and rejection (for false classes) performance. New aspects of this paper are that we provide remarks on our hierarchical design method, including our hierarchical clustering rule, and discuss the meaning and the use of probabilities in our soft-decision hierarchical SVRDM classifiers. We also provide initial tests results on a new database (COIL) that allows large class problem to be addressed. No prior work considered rejection of false classes on this database.
Keywords :
decision theory; generalisation (artificial intelligence); pattern classification; pattern clustering; probability; support vector machines; generalization; hierarchical soft binary-decision classifier; multiclass classification; multiclass rejection; pattern recognition; probability; support vector hierarchical clustering method; support vector representation-discrimination machine classifier; Clustering methods; Databases; Design methodology; Large-scale systems; Neural networks; Object recognition; Pattern recognition; Support vector machine classification; Support vector machines; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2009. IJCNN 2009. International Joint Conference on
Conference_Location :
Atlanta, GA
ISSN :
1098-7576
Print_ISBN :
978-1-4244-3548-7
Electronic_ISBN :
1098-7576
Type :
conf
DOI :
10.1109/IJCNN.2009.5178670
Filename :
5178670
Link To Document :
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