Classifying under computational resource constraints: anytime classification using probabilistic estimators
暂无分享,去创建一个
[1] Paul Resnick,et al. Recommender systems , 1997, CACM.
[2] Eamonn J. Keogh,et al. Learning the Structure of Augmented Bayesian Classifiers , 2002, Int. J. Artif. Intell. Tools.
[3] Bernard Toursel,et al. Distributed Data Mining , 2001, Scalable Comput. Pract. Exp..
[4] Leo Breiman,et al. Bias, Variance , And Arcing Classifiers , 1996 .
[5] H. Akaike. A new look at the statistical model identification , 1974 .
[6] Pat Langley,et al. An Analysis of Bayesian Classifiers , 1992, AAAI.
[7] Shlomo Zilberstein,et al. Attribute measurement policies for time and cost sensitive classification , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).
[8] Thomas G. Dietterich,et al. Error-Correcting Output Coding Corrects Bias and Variance , 1995, ICML.
[9] David W. Opitz,et al. An anytime approach to connectionist theory refinement - refining the topologies of knowledge-based neural networks , 1996, Technical Report / University of Wisconsin, Madison / Computer Sciences Department.
[10] Geoffrey I. Webb,et al. Not So Naive Bayes: Aggregating One-Dependence Estimators , 2005, Machine Learning.
[11] Eric Horvitz,et al. Reasoning under Varying and Uncertain Resource Constraints , 1988, AAAI.
[12] Dennis DeCoste,et al. Anytime Interval-Valued Outputs for Kernel Machines: Fast Support Vector Machine Classification via Distance Geometry , 2002, ICML.
[13] Catherine Blake,et al. UCI Repository of machine learning databases , 1998 .
[14] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[15] Ron Kohavi,et al. Bias Plus Variance Decomposition for Zero-One Loss Functions , 1996, ICML.
[16] Geoffrey I. Webb,et al. # 2001 Kluwer Academic Publishers. Printed in the Netherlands. Machine Learning for User Modeling , 1999 .
[17] Ron Kohavi,et al. Wrappers for Feature Subset Selection , 1997, Artif. Intell..
[18] Mehran Sahami,et al. Learning Limited Dependence Bayesian Classifiers , 1996, KDD.
[19] Kevin B. Korb,et al. Bayesian Artificial Intelligence , 2004, Computer science and data analysis series.
[20] David D. Lewis,et al. Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval , 1998, ECML.
[21] John J. Grefenstette,et al. An Approach to Anytime Learning , 1992, ML.
[22] Daphne Koller,et al. Toward Optimal Feature Selection , 1996, ICML.
[23] Ian H. Witten,et al. Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .
[24] Ian H. Witten,et al. Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.
[25] Ian Witten,et al. Data Mining , 2000 .
[26] Pat Langley,et al. Induction of Selective Bayesian Classifiers , 1994, UAI.
[27] Thomas G. Dietterich. What is machine learning? , 2020, Archives of Disease in Childhood.
[28] Xindong Wu,et al. Induction By Attribute Elimination , 1999, IEEE Trans. Knowl. Data Eng..
[29] Benjamin W. Wah,et al. Population-Based Learning: A Method for Learning from Examples Under Resource Constraints , 1992, IEEE Trans. Knowl. Data Eng..
[30] Joe Suzuki,et al. Learning Bayesian Belief Networks Based on the MDL Principle : An Efficient Algorithm Using the Branch and Bound Technique , 1999 .
[31] Shlomo Zilberstein,et al. Anytime algorithm development tools , 1996, SGAR.
[32] Janez Demsar,et al. Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..
[33] Salvatore J. Stolfo,et al. Distributed data mining in credit card fraud detection , 1999, IEEE Intell. Syst..
[34] Shlomo Zilberstein,et al. Scheduling contract algorithms on multiple processors , 2002, AAAI/IAAI.
[35] Peter D. Turney. Types of Cost in Inductive Concept Learning , 2002, ArXiv.
[36] Geoffrey I. Webb,et al. MultiBoosting: A Technique for Combining Boosting and Wagging , 2000, Machine Learning.
[37] Jerome H. Friedman,et al. On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality , 2004, Data Mining and Knowledge Discovery.