A probabilistic active support vector learning algorithm

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 26; no. 3; pp. 413 - 418
• Main Authors: Mitra, P., Murthy, C.A., Pal, S.K.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2004; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Artificial Intelligence; Breast Neoplasms - diagnosis; Cluster Analysis; Computing Methodologies; Design optimization; Diagnosis, Computer-Assisted - methods; Humans; Image Interpretation, Computer-Assisted - methods; Information Storage and Retrieval - methods; Iterative algorithms; Lagrangian functions; Large-scale systems; Machine learning; Models, Statistical; Numerical Analysis, Computer-Assisted; Pattern Recognition, Automated; Quadratic programming; Reproducibility of Results; Robustness; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Subtraction Technique; Support vector machine classification; Support vector machines
• ISSN: 0162-8828; 1939-3539
• DOI: 10.1109/TPAMI.2004.1262340

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.