Syed N A, Liu H, Sung K K. Incremental learning with support vector machines[C]. Proc of Int Joint Conf on Artificial Intelligence. San Jose, 1999: 641-642.
[2]
Mitra P, Murthy C A, Pal S K. Data condensation in large databases by incremental learning with support vector machines[C]. Proc of the 15th Int Conf on Pattern Recognition. Los Alamitos, 2000: 708-711.
[3]
Okamato K, Ozawa S, Abe S. A fast incremental learning algorithm of RBF networks with long-term memory[C]. Proc of the Int Joint Conf on Neural Networks. Portland, 2003: 102-107.
[4]
Zhu Z F, Zhu X Q, Guo Y F. Inverse matrix-free incremental proximal support vector machine[J]. Decision Support Systems, 2012, 53(3): 395-405.
[5]
Zheng J, Shen F R, Fan H J. An online incremental learning support vector machine for large-scale data[J]. Neural Computing and Applications, 2013, 22(5): 1023-1035.
[6]
Du H L, Teng S H, Zhu Q F. Fast SVM incremental learning based on clustering algorithm[C]. Intelligent Computing and Intelligent Systems. Piscataway, 2009: 13-17.
[7]
Schlimmer J C, Granger Jr R H. Incremental learning from noisy data[J]. Machine Learning, 1986, 1(3): 317-354.
[8]
Zhang L, Zhou W D, Jiao L C. Pre-extracting support vectors for support vector machine[C]. Proc of the 5th Int Conf on Signal Processing. Beijing: 2000: 1432-1435.
[9]
Cauwenberghs G, Poggio T. Incremental and decremental support vector machine learning[J]. Advances in Neural Information Processing Systems, 2001, 44(13): 409-415.
[10]
Li Z Y, Zhang J F, Hu S S. Incremental support vector machine algorithm based on multi-kernel learning[J]. J of Systems Engineering and Electronics, 2011, 22(4): 702-706.
[11]
Elwell R, Polikar R. Incremental learning of concept drift in nonstationary environments[J]. Neural Networks, 2011, 22(10): 1517-1531.
[12]
Gardenfors, Peter. Belief revision[M]. London: Cambridge University Press, 2003: 1-9.
[13]
Alchourron C E, Gardenfors P, Makinson D. On the logic of theory change: Partial meet contraction and revision functions[J]. J of Symbolic Logic, 1985, 50(2): 510-530.
[14]
Vapnik V. The nature of statistical learning theory[M]. New York: Springer-Verlag, 1995: 5-13.
[15]
Maynard-Reid I I P, Shoham Y. Belief fusion: Aggregating pedigreed belief states[J]. J of Logic, Language and Information, 2001, 10(2): 183-209.
[16]
Kfir-Dahav N E, Tennenholtz M. Multi-agent belief revision[C]. Proc of the 6th Conf on Theoretical Aspects of Rationality and Knowledge. San Francisco, 1996: 175-194.
[17]
Satoh K, Yamamoto K. Speculative computation with multi-agent belief revision[C]. Proc of the 1st Int Joint Conf on Autonomous Agents and Multiagent Systems. Bologna, 2002: 897-904.
[18]
Wagstaff K, Cardie C, Rogers S. Constrained ??-means clustering with background knowledge[C]. Proc of the 18th Int Conf on Machine Learning. Washington DC, 2001: 577-584.
[19]
Dubois D, Lang J, Prade H. Automated reasoning using possibilistic logic: Semantics, belief revision, and variable certainty weights[J]. Knowledge and Data Engineering, 1994, 6(1): 64-71.
