Title
Minimum classification error training with geometric margin enhancement for robust pattern recognition
Abstract
As a practical discriminative approach to pattern classifier design, the Minimum Classification Error (MCE) training method has been extensively used. In it, classification correctness is represented by a misclassification measure whose positive value corresponds to misclassification and whose negative value corresponds to correct classification. The amount of its negative value is considered to bring in high robustness to unseen pattern samples. However, this effect of the misclassification measure on robustness increase has been questioned in recent studies. In this paper, we clarify the cause of the measure's insufficiency and propose a solution by developing a new MCE training method using geometric margin as the misclassification measure. To maintain the high application generality of the MCE framework, we derive the geometric margin for a general class of discriminant functions and demonstrate the utility of our new MCE method by installing the newly formulated general geometric margin to widely used prototype-based discriminant functions.
Year
DOI
Venue
2011
10.1109/MLSP.2011.6064639
Machine Learning for Signal Processing
Keywords
Field
DocType
pattern classification,probability,mce training method,classification correctness,geometric margin enhancement,minimum classification error training,misclassification measure,pattern classifier design,robust pattern recognition,discriminative training,geometric margin,minimum classification error,discriminant function,pattern recognition
Pattern recognition,Discriminant,Computer science,Correctness,Robustness (computer science),Artificial intelligence,Classifier (linguistics),Discriminative model,Machine learning,Generality,Bayes classifier
Conference
ISSN
ISBN
Citations 
1551-2541 E-ISBN : 978-1-4577-1622-5
978-1-4577-1622-5
1
PageRank 
References 
Authors
0.35
16
3
Name
Order
Citations
PageRank
Hideyuki Watanabe1378.46
Shigeru Katagiri2850114.01
Miho Ohsaki319528.23