Detecting Concept Drift with Support Vector Machines
暂无分享,去创建一个
[1] Roger Fletcher,et al. Practical methods of optimization; (2nd ed.) , 1987 .
[2] R. Fletcher. Practical Methods of Optimization , 1988 .
[3] Gerard Salton,et al. Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..
[4] Ronald L. Rivest,et al. Learning Time-Varying Concepts , 1990, NIPS.
[5] IJsbrand Jan Aalbersberg,et al. Incremental relevance feedback , 1992, SIGIR '92.
[6] Tom M. Mitchell,et al. Experience with a learning personal assistant , 1994, CACM.
[7] James Allan,et al. Incremental relevance feedback for information filtering , 1996, SIGIR '96.
[8] Marko Balabanovic,et al. An adaptive Web page recommendation service , 1997, AGENTS '97.
[9] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[10] Ingrid Renz,et al. Adaptive Information Filtering: Learning in the Presence of Concept Drifts , 1998 .
[11] J. C. BurgesChristopher. A Tutorial on Support Vector Machines for Pattern Recognition , 1998 .
[12] Thorsten Joachims,et al. Making large scale SVM learning practical , 1998 .
[13] B. Schölkopf,et al. Advances in kernel methods: support vector learning , 1999 .
[14] Huan Liu,et al. Handling concept drifts in incremental learning with support vector machines , 1999, KDD '99.
[15] Ralf Klinkenberg. Maschinelle Lernverfahren zum adaptiven Informationsfiltern bei sich verändernden Konzepten , 2000 .
[16] Thorsten Joachims,et al. Estimating the Generalization Performance of an SVM Efficiently , 2000, ICML.