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Note: Links lead to the DBLP on the Web. Rich Caruana Rich Caruana, Alexandru Niculescu-Mizil , Geoff Crew , Alex Ksikes : Ensemble selection from libraries of models. ICML 2004 Rich Caruana, Alexandru Niculescu-Mizil : Data mining in metric space: an empirical analysis of supervised learning performance criteria. KDD 2004 : 69-78 Rich Caruana, Alexandru Niculescu-Mizil : An Empirical Evaluation of Supervised Learning for ROC Area. ROCAI 2004 : 1-8 Rich Caruana, Alexandru Niculescu-Mizil : Data Mining in Metric Space: An Empirical Analysis of Supervised Learning Performance Criteria. ROCAI 2004 : 9-18 Rich Caruana, Virginia R. de Sa : Benefitting from the Variables that Variable Selection Discards. Journal of Machine Learning Research 3 : 1245-1264 (2003) John Langford , Rich Caruana: (Not) Bounding the True Error. NIPS 2001 : 809-816 Joseph O'Sullivan , John Langford , Rich Caruana, Avrim Blum : FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness. ICML 2000 : 703-710 Rich Caruana, Steve Lawrence , C. Lee Giles : Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping. NIPS 2000 : 402-408 Adam L. Berger , Rich Caruana, David Cohn , Dayne Freitag , Vibhu O. Mittal : Bridging the lexical chasm: statistical approaches to answer-finding. SIGIR 2000 : 192-199 Gregory F. Cooper , Constantin F. Aliferis , R. Ambrosino , John M. Aronis , Bruce G. Buchanan , Rich Caruana, M. J. Fine , Clark Glymour , G. Gordon , B. H. Hanusa , J. E. Janosky , Christopher Meek , Tom M. Mitchell , Thomas Richardson , Peter Spirtes : An evaluation of machine-learning methods for predicting pneumonia mortality. Artificial Intelligence in Medicine 9 (2): 107-138 (1997) Rich Caruana: Multitask Learning. Machine Learning 28 (1): 41-75 (1997) Rich Caruana: Algorithms and Applications for Multitask Learning. ICML 1996 : 87-95 Rich Caruana, Virginia R. de Sa : Promoting Poor Features to Supervisors: Some Inputs Work Better as Outputs. NIPS 1996 : 389-395 Rich Caruana: A Dozen Tricks with Multitask Learning. Neural Networks: Tricks of the Trade 1996 : 165-191 Shumeet Baluja , Rich Caruana: Removing the Genetics from the Standard Genetic Algorithm. ICML 1995 : 38-46 Rich Caruana, Shumeet Baluja , Tom M. Mitchell : Using the Future to Sort Out the Present: Rankprop and Multitask Learning for Medical Risk Evaluation. NIPS 1995 : 959-965 Rich Caruana, Dayne Freitag : Greedy Attribute Selection. ICML 1994 : 28-36 Rich Caruana: Learning Many Related Tasks at the Same Time with Backpropagation. NIPS 1994 : 657-664 Tom M. Mitchell , Rich Caruana, Dayne Freitag , John P. McDermott , David Zabowski : Experience with a Learning Personal Assistant. Commun. ACM 37 (7): 80-91 (1994) Rich Caruana: Multitask Learning: A Knowledge-Based Source of Inductive Bias. ICML 1993 : 41-48 Larry J. Eshelman , Rich Caruana, J. David Schaffer : Biases in the Crossover Landscape. ICGA 1989 : 10-19 J. David Schaffer , Rich Caruana, Larry J. Eshelman , Rajarshi Das : A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization. ICGA 1989 : 51-60 Rich Caruana, Larry J. Eshelman , J. David Schaffer : Representation and Hidden Bias II: Eliminating Defining Length Bias in Genetic Search via Shuffle Crossover. IJCAI 1989 : 750-755 Rich Caruana, J. David Schaffer , Larry J. Eshelman : Using Multiple Representations to Improve Inductive Bias: Gray and Binary Coding for Genetic Algorithms. ML 1989 : 375-378 Rich Caruana, J. David Schaffer : Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms. ML 1988 : 153-161 Rich Caruana: The automatic training of rule bases that use numerical uncertainty representations. Int. J. Approx. Reasoning 2 (3): 330-331 (1988) Rich Caruana: The Automatic Training of Rule Bases that Use Numerical Uncertainty Representations. UAI 1987 : 347-356 1 [ 18 ] 2 [ 18 ] 3 [ 18 ] 4 [ 12 ] [ 13 ] 5 [ 19 ] 6 [ 21 ] 7 [ 18 ] 8 [ 19 ] 9 [ 18 ] 10 [ 27 ] 11 [ 6 ] 12 [ 4 ] [ 5 ] [ 6 ] [ 7 ] 13 [ 18 ] 14 [ 9 ] [ 11 ] [ 19 ] 15 [ 20 ] 16 [ 18 ] 17 [ 18 ] 18 [ 18 ] 19 [ 18 ] 20 [ 27 ] 21 [ 21 ] [ 22 ] 22 [ 20 ] 23 [ 9 ] 24 [ 18 ] 25 [ 9 ] [ 12 ] [ 18 ] 26 [ 19 ] 27 [ 24 ] [ 25 ] [ 26 ] [ 27 ] 28 [ 21 ] 29 [ 18 ] 30 [ 15 ] [ 23 ] 31 [ 3 ] [ 4 ] [ 5 ] [ 6 ] [ 7 ] 32 [ 18 ] 33 [ 9 ]  ©2005 Association for Computing Machinery |
