A probabilistic active support vector learning algorithm.
The paper describes a probabilistic active learning strategy for support vector machine (SVM) design in large data applications. The learning strategy is motivated by the statistical query model. While most existing methods of active SVM learning query for points based on their proximity to the current separating hyperplane, the proposed method queries for a set of points according to a distribution as determined by the current separating hyperplane and a newly defined concept of an adaptive confidence factor. This enables the algorithm to have more robust and efficient learning capabilities. The confidence factor is estimated from local information using the k nearest neighbor principle. The effectiveness of the method is demonstrated on real-life data sets both in terms of generalization performance, query complexity, and training time.
Mitra
Pabitra
P
Machine Intelligence Unit, Indian Statistical Institute, Calcutta 700 108, India. pabitra_r@isical.ac.in
Murthy
C A
CA
Pal
Sankar K
SK
D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000175:diagnosis*; D016000:Cluster Analysis; D003205:Computing Methodologies; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods*; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique