Aha, D. W. (1992). Generalizing from case studies: A case study. In Proceedings of the Ninth International Workshop on Machine Learning (pp. 1–10), Morgan Kaufman.
Baltes, J., & MacDonald, B. (1992). Case-based meta learning: Sustained learning supported by a dynamically biased version space. In Proceedings of the ML-92 Workshop on Biases in Inductive Learning.
Baxter, J. (1998). Theoretical models of learning to learn. Learning to Learn (Chap. 4, pp. 71–94), Kluwer Academic Publishers, MA.
Bensusan, H. (1998). God doesn't always shave with Occam's Razor—Learning when and how to prune. In Proceedings of the Tenth European Conference on Machine Learning (pp. 119–124), Springer.
Bensusan, H., & Giraud-Carrier, C. (2000). Casa Batlo in Passeig or landmarking the expertise space. In Proceedings of the ECML-2000 Workshop on Meta-Learning: Building Automatic Advice Strategies for Model Selection and Method Combination (pp. 29–46), Barcelona, Spain.
Brazdil, P., Soares, C., & Pinto da Costa, J. (2003). Ranking learning algorithms: Using IBL and meta-learning on accuracy and time results. Machine Learning, 50:3, 251–277.
Brazdil, P. (1998). Data transformation and model selection by experimentation and meta-learning. In Proceedings of the ECML-98 Workshop on Upgrading Learning to Meta-Level: Model Selection and Data Transformation (pp. 11–17), Technical University of Chemnitz.
Brodley, C. E. (1995). Recursive automatic bias selection for classifier construction. Machine Learning, 20:1, 63–94.
Caruana, R. (1997). Multitask Learning. Second Special Issue on Inductive Transfer. Machine Learning, 28:1, 41–75.
Chan, P. K., & Stolfo, S. (1998). On the accuracy of meta-learning for scalable data mining. Journal of Intelligent Integration of Information 8, 3–28.
DesJardins, M., & Gordon, D. F. (1995). Evaluation and selection of biases in machine learning. Machine Learning, 20:1, 5–22.
Dzeroski, S., & Zenko, B. (2004). Is combining classifiers better than selecting the best one? Machine Learning, 54:3, 195–209.
Gama, J., & Brazdil, P. (1995). Characterization of classification algorithms. In Proceedings of the Seventh Portuguese Conference on Artificial Intelligence (EPIA) (189–200), Funchal, Madeira Island, Portugal.
Geman, S., Bienenstock, E., & Doursat, R. (1991). Neural networks and the bias/variance dilemma. Neural Computation, 4, 1–58.
Gordon, D. F. (1990). Active bias adjustment for incremental, supervised concept learning. PhD Thesis, University of Maryland, 1990.
Hastie, T., Tibshirani, R., & Friedman, J. (2001). The Elements of Statistical Learning; Data Mining, Inference, and Prediction. Springer-Verlag.
Kalousis, A., Gama, J., & Hilario, M. (2004). On data and algoritms: Understanding learning performance. Machine Learning, 54:3, 195–209.
Keller, J., Paterson, I., & Berrer, H. (2000). An integrated concept for multi-crieria-ranking of data-mining algorithms. In Proceedings of the ECML-2000 Workshop on Meta-Learning: Building Automatic Advice Strategies for Model Selection and Method Combination (pp. 73–86), Barcelona, Spain.
Merz, C. J. (1995). Dynamical selection of learning algorithms. In Learning from Data: Artificial Intelligence and Statistics, D. Fisher & H. J. Lenz (Eds.), Springer-Verlag.
Michie, D., Spiegelhalter, D. J., & Taylor, C. C. (1994). Machine Learning, Neural and Statistical Classification. Ellis Horwood, Chichester, England.
Ortega, J., Koppel, M., & Argamon, S. (2001). Arbitrating among competing classifiers using learned referees. Knowledge and Information Systems, 3, 470–490.
Peng, W., Flach, P. A., Soares, C., & Brazdil, P. (2002). Improved Data Set Characterisation for Meta-learning. In Proceedings of the Fifth International Conference on Discovery Science, LNAI 2534, 141–152.
Pfahringer, B., Bensusan, H., & Giraud-Carrier, C. (2000). Meta-learning by landmarking various learning algorithms. In Proceedings of the Seventeenth International Conference on Machine Learning (743–750), Stanford, CA.
Pratt, L., & Thrun, S. (1997). Second Special Issue on Inductive Transfer. Machine Learning, 28(1).
Rendell, L., Seshu, R., & Tcheng, D. (1987a). More robust concept learning using dynamically-variable bias. In Proceedings of the Fourth International Workshop on Machine Learning (pp. 66–78), Morgan Kaufman.
Rendell, L., Seshu, R., & Tcheng, D. (1987b). Layered concept-learning and dynamically-variable bias management. In Proceedings of the International Joint Conference of Artificial Intelligence, (pp. 308–314), Milan, Italy.
Schmidhuber, J. (2004). Optimal ordered problem solver. Machine Learning, 54:3, 195–209.
Soares, C., Brazdil, P., & Kuba, P. (2004). A meta-learning approach to select the kernel width in support vector regression, Machine Learning, 54:3, 195–209.
Stone, P., & Veloso, M. (2000). Layered learning, In Proceedings of the Eleventh European Conference on Machine Learning (pp. 369–381) Barcelona, Spain.
Todorovski, L., & Dzeroski, S. (2003). Combining classifiers with meta decision trees Machine Learning, 50:3, 223–250.
Thrun, S., & Mitchell, T. (1995). Learning one more thing. In Proceedings of the International Joint Conference on Artificial Intelligence (pp. 1217–1223), Morgan Kaufman.
Thrun, S. (1998). Lifelong learning algorithms. Learning to Learn (Chap. 8, pp. 181–209), MA: Kluwer Academic Publishers.
Utgoff, P. (1986). Shift of bias for inductive concept learning. In R. S. Michalski, et al. (Ed.), Machine Learning: An Artificial Intelligence Approach, Vol. II (pp. 107–148), California: Morgan Kaufman.
Utgoff, P., & Stracuzzi, D. J. (2003). Many-layered learning. Neural Networks, 14, 2497–2529, MIT Press.
Vilalta, R., & Drissi, Y. (2002). A perspective view and survey of meta-learning. Journal of Artificial Intelligence Review, 18:2, 77–95.
Widmer, G., & Kubat, M. (1996). Learning in the presence of concept drift and hidden contexts. Machine Learning, 23:1, 69–101.
Widmer, G. (1997). Tracking context changes through meta-learning. Machine Learning, 27:3, 259–286.
Wolpert, D. (1992). Stacked Generalization. Neural Networks, 5, 241–259.