Transfer Incremental Learning for Pattern Classification

Publication Type:
Conference Proceeding
Proceedings of the 19th ACM Conference on Information and Knowledge Management & Co-Located Workshops (CIKM 2010), 2010, pp. 1709 - 1712
Issue Date:
Full metadata record
Files in This Item:
Filename Description SizeFormat
2010001767OK.pdf687.87 kBAdobe PDF
Traditional machine learning methods, such as Support Vector Machines (SVMs), usually assume that training and test data share the same distributions. Due to the inherent dynamic data nature, it is often observed that (1) the volumes of the training data may gradually grow; and (2) the existing and the newly arrived samples may be subject to different distributions or learning tasks. In this paper, we propose a Transfer Incremental Support Vector Machine(TrISVM), with the objective of tackling changes in data volumes and learning tasks at the same time. By using new updating rules to calculate the inverse matrix, TrISVM solves the existing incremental learning problem more efficiently, especially for high dimensional data. Furthermore, when using new samples to update the existing models, TrISVM employs sample-based weight adjustment procedures to ensure that the concept transferring between auxiliary and target samples can be leveraged to fulfill the transfer learning goal. Experimental results on real-world data sets demonstrate that TrISVM achieves better efficiency and prediction accuracy than both incremental-learning and transfer-learning based methods. In addition, the results also show that TrISVM is able to achieve bidirectional knowledge transfer between two similar tasks.
Please use this identifier to cite or link to this item: