Learning in the presence of concept drift and hidden contexts
暂无分享,去创建一个
[1] Ryszard S. Michalski,et al. A Theory and Methodology of Inductive Learning , 1983, Artificial Intelligence.
[2] Leslie G. Valiant,et al. A theory of the learnable , 1984, STOC '84.
[3] Stephen Grossberg,et al. Competitive Learning: From Interactive Activation to Adaptive Resonance , 1987, Cogn. Sci..
[4] Michael Lebowitz,et al. Experiments with incremental concept formation: UNIMEM , 2004, Machine Learning.
[5] Pat Langley,et al. Hill-Climbing Theories of Learning , 1987 .
[6] Allen Newell,et al. Some Chunks Are Expensive , 1988, ML.
[7] Shaul Markovitch,et al. The Role of Forgetting in Learning , 1988, ML.
[8] Dana Angluin,et al. Queries and concept learning , 1988, Machine Learning.
[9] Miroslav Kubat. Floating approximation in time-varying knowledge bases , 1989, Pattern Recognit. Lett..
[10] David Haussler,et al. Learnability and the Vapnik-Chervonenkis dimension , 1989, JACM.
[11] Ronald L. Rivest,et al. Learning Time-Varying Concepts , 1990, NIPS.
[12] Steven Minton,et al. Quantitative Results Concerning the Utility of Explanation-based Learning , 1988, Artif. Intell..
[13] Philip M. Long,et al. Tracking drifting concepts using random examples , 1991, Annual Conference Computational Learning Theory.
[14] Andrew W. Moore,et al. Fast, Robust Adaptive Control by Learning only Forward Models , 1991, NIPS.
[15] Wolfgang Maass,et al. On-line learning with an oblivious environment and the power of randomization , 1991, COLT '91.
[16] Ronald L. Rivest,et al. Incrementally Learning Time-Varying Half Planes , 1991, NIPS.
[17] Miroslav Kubat,et al. FAVORIT: Concept formation with ageing of knowledge , 1992, Pattern Recognit. Lett..
[18] Philip M. Long,et al. Apple tasting and nearly one-sided learning , 1992, Proceedings., 33rd Annual Symposium on Foundations of Computer Science.
[19] Miroslav Kubat. A machine learning-based approach to load balancing in computer networks , 1992 .
[20] Gerhard Widmer,et al. Learning Flexible Concepts from Streams of Examples: FLORA 2 , 1992, ECAI.
[21] Miroslav Kubat. Conceptual Inductive Learning: The Case of Unreliable Teachers , 1992, Artif. Intell..
[22] Marcos Salganicoff,et al. Density-Adaptive Learning and Forgetting , 1993, ICML.
[23] Pat Langley,et al. Average-Case Analysis of a Nearest Neighbor Algorithm , 1993, IJCAI.
[24] Gerhard Widmer,et al. Effective Learning in Dynamic Environments by Explicit Context Tracking , 1993, ECML.
[25] Fredrik Kilander,et al. COBBIT - A Control Procedure for COBWEB in the Presence of Concept Drift , 1993, ECML.
[26] Miroslav Kubat,et al. Flexible concept learning in real-time systems , 1993, J. Intell. Robotic Syst..
[27] Marcos Salganicoff,et al. Explicit Forgetting Algorithms for Memory Based Learning , 1993 .
[28] Gerhard Widmer. Combining Robustness and Flexibility in Learning Drifting Concepts , 1994, ECAI.
[29] Peter D. Turney. Robust Classification with Context-Sensitive Features , 2002, ArXiv.
[30] J. C. Schlimmer,et al. Incremental learning from noisy data , 2004, Machine Learning.
[31] Tom M. Mitchell,et al. Explanation-Based Generalization: A Unifying View , 1986, Machine Learning.
[32] Philip M. Long,et al. Tracking drifting concepts by minimizing disagreements , 2004, Machine Learning.
[33] Steven Salzberg,et al. A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features , 2004, Machine Learning.
[34] D. Kibler,et al. Instance-based learning algorithms , 2004, Machine Learning.
[35] Leslie Pack Kaelbling,et al. Associative Reinforcement Learning: Functions in k-DNF , 1994, Machine Learning.
[36] Steven Salzberg,et al. A Nearest Hyperrectangle Learning Method , 1991, Machine Learning.