Aha, D. W. (1990). A Study of Instance-Based Algorithms for Supervised Learning Tasks: Mathematical, Empirical and Psychological Evaluations. PhD. Thesis; Technical Report No. 90–42, University of California, Irvine.Google Scholar
Atkeson, C. G., Moore, A. W. & Schaal, S. A. (1997). Locally Weighted Learning. AI Review, this issue.
Atkeson, C. G. (1990). Memory-Based Approaches to Approximating Continuous Functions. In 1990 Workshop on Nonlinear Modeling and Forecasting. Adison-Wesley.
Bottou, L. & Vapnik, V. (1992). Local Learning Algorithms. Neural Computation
: 888–900.Google Scholar
Box, G. E. P., Hunter, W. G. & and Hunter, J. S. (1978). Statistics for Experimenters. Wiley.
Caruana, R. A. & and Freitag, D. (1994). Greedy Attribute Selection. In Machine Learning: Proceedings of the Eleventh International Conference, pp. 28–36. Morgan Kaufmann.
Cleveland, W. S., Devlin, S. J. & Grosse, E. (1988). Regression by local fitting: Methods, properties, and computational algorithms. Journal of Econometrics
: 87–114.Google Scholar
Conte, S. D. & De Boor, C. (1980). Elementary Numerical Analysis. McGraw Hill.
Dasarathy, B. V. (1991). Nearest Neighbor Norms: NN Patern Classifaction Techniques. IEEE Computer Society Press.
Efron, B. & Tibshirani, R. (1991). Statistical Data Analysis in the Computer Age. Science
: 390–395.Google Scholar
Fix, E. & Hodges, J. L. (1951). Discriminatory Analysis: Nonparametric Discrimination: Consistency Properties. Project 21–49–004, Report Number 4, USAF School of Aviation Medicine.
Goldberg, D. (1989). Genetic Algorithms in Search, Optimization and Machine Learning
. Reading, MA: Addison-Wesley.Google Scholar
Gratch, J., Chien, S. & DeJong, G. (1993). Learning Search Control Knowledge for Deep Space Network Scheduling. In Proceedings of the 10th International Conference on Machine Learning, pp. 135–142. Morgan Kaufmann.
Gratch, J. (1994). An effective method for correlated selection problems. Department of Computer Science Technical Report Num. 1893, University of Illinois at Urbana-Champaign.Google Scholar
Greiner, R. & Jurisca, I. (1992). A statistical approach to solving the EBL utility problem. In Proceedings of the Tenth International conference on Artificial Intelligence, pp. 241–248. MIT Press.
Hastie, T. J. & Tibshirani, R. J. (1990). Generalized additive models. Chapman and Hall.
Haussler, D. (1992). Decision theoretic generalizations of the pac model for neural net and other learning applications. Information and Computation
: 78–150.Google Scholar
Hoeffding, W. (1963). Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association
: 13–30.Google Scholar
John, G. H., Kohavi, R. & Pfleger, K. (1994). Irrelevant features and the Subset Selection Problem. In Machine Learning: Proceedings of the Eleventh International Conference, pp. 121–129. Morgan Kaufmann.
Kaelbling, L. P. (1990). Learning in Embedded Systems. PhD. Thesis; Technical Report No. TR–90–04, Stanford University, Department of Computer Science.
Kreider, J. F. & Haberl, J. S. (1994). Predicting hourly building energy usage: The great energy predictor shootout — Overview and discussion of results. Transactions of the American Society of Heating, Refrigerating and Air-Conditioning Engineers, 100, Part 2.
Lowe, D. G. (1995). Similarity metric learning for a variable-kernel classifier. Neural Computation
: 72–85.Google Scholar
Maron, O. & Moore, A. W. (1994). Hoeffding Races: Accelerating model selection search for classification and function approximation. In Cowan, J. D., Tesauro, G. & Alspector, J. (eds.), Advances in Neural Information Processing Systems 6. Morgan Kaufmann.
Maron, O. (1994). Hoeffding Races: Model Selection for MRI Classification. Masters Thesis, Dept. of Electrical Engeineering and Computer Science, M.I.T.
Miller, A. J. (1990). Subset Selection in Regression. Chapman and Hall.
Moore, A. W. & Lee, M. S. (1994). Efficient Algorithms for Minimizing Cross Validation Error. In Machine Learning: Proceedings of the Eleventh International Conference, pp. 190–198. Morgan Kaufmann.
Moore, A. W., Hill, D. J. & Johnson, M. P. (1992). An empirical investigation of brute force to choose features, smoothers and function approximators. In Hanson, S., Judd, S. & Petsche, T. (eds.), Computational Learning Theory and Natural Learning Systems, Volume 3. MIT Press.
Moore, A. W. (1992). Fast, robust adaptive control by learning only forward models. In Moody, J. E., Hanson, S. J. & Lippman, R. P. (eds.), Advances in Neural Information Processing Systems 4. Morgan Kaufmann.
Murphy, P. M. (1996). UCI repository of machine learning databases. For more information contact firstname.lastname@example.org.
Omohundro, S. (1993). Private communication.
Press, W. H., Teukolsky, S. A., Vetterling, W. T. & Flannery, B. P. (1992). Numerical Recipes in C: the art of scientific computing
. New York: Cambridge University Press, second edition.Google Scholar
Rivest, R. L. & Yin, Y. (1993). Simulation Results for a new two-armed bandit heuristic. Technical report, Laboratory for Computer Science, M.I.T.
Schaal, S. & Atkeson, C. G. (1993). Open loop stable control strategies for robot juggling. In Proceedings of IEEE conference on Robotics and Automation.
Schmitt, S. A. (1969). Measuring Uncertainty: An elementary introduction to Bayesian Statistics. Addison-Wesley.
Skalak, D. B. (1994). Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms. In Machine Learning: Proceedings of the Eleventh International Conference, pp. 293–301. Morgan Kaufmann.
Weiss, S. M. & Kulikowski, C. A. (1991). Computer systems that learn: Classification and prediction methods from statistics, neural nets, machine learning, and expert systems
. San Mateo, CA: Morgan-Kaufmann.Google Scholar
Welch, B. L. (1937). The significance of the difference between two means when the population variances are unequal. Biometrika
Zhang, X, Mesirov, J. P. & Waltz, D. L. (1992). Hybrid system for protein secondary structure prediction. Journal of Molecular Biology
: 1049–1063.Google Scholar