Learning to Learn: Introduction and Overview
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[1] R. Franke. Scattered data interpolation: tests of some methods , 1982 .
[2] Leslie G. Valiant,et al. A theory of the learnable , 1984, STOC '84.
[3] Bernard Silver,et al. Using meta-level inference to constrain search and to learn strategies in equation solving , 1984 .
[4] Paul E. Utgoff,et al. Shift of bias for inductive concept learning , 1984 .
[5] Nichael Lynn Cramer,et al. A Representation for the Adaptive Generation of Simple Sequential Programs , 1985, ICGA.
[6] David L. Waltz,et al. Toward memory-based reasoning , 1986, CACM.
[7] Paul E. Utgoff,et al. Machine Learning of Inductive Bias , 1986 .
[8] David Haussler,et al. Occam's Razor , 1987, Inf. Process. Lett..
[9] Gerald DeJong,et al. Schema Acquisition from One Example: Psychological Evidence for Explanation-Based Learning. , 1987 .
[10] David Tcheng,et al. MORE ROBUST CONCEPT LEARNING USING DYNAMICALLY – VARIABLE BIAS , 1987 .
[11] Larry A. Rendell,et al. Layered Concept-Learning and Dynamically Variable Bias Management , 1987, IJCAI.
[12] Leslie G. Valiant,et al. A general lower bound on the number of examples needed for learning , 1988, COLT '88.
[13] Andrew W. Moore,et al. Efficient memory-based learning for robot control , 1990 .
[14] S. C. Suddarth,et al. Rule-Injection Hints as a Means of Improving Network Performance and Learning Time , 1990, EURASIP Workshop.
[15] Stuart J. Russell. Prior knowledge and autonomous learning , 1991, Robotics Auton. Syst..
[16] Christopher G. Atkeson,et al. Using locally weighted regression for robot learning , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.
[17] Steven C. Suddarth,et al. Symbolic-Neural Systems and the Use of Hints for Developing Complex Systems , 1991, Int. J. Man Mach. Stud..
[18] Yann LeCun,et al. Tangent Prop - A Formalism for Specifying Selected Invariances in an Adaptive Network , 1991, NIPS.
[19] Geoffrey E. Hinton,et al. Feudal Reinforcement Learning , 1992, NIPS.
[20] Gerald DeJong. Investigating Explanation-Based Learning , 1992 .
[21] T. Poggio,et al. Recognition and Structure from one 2D Model View: Observations on Prototypes, Object Classes and Symmetries , 1992 .
[22] Satinder Singh. Transfer of Learning by Composing Solutions of Elemental Sequential Tasks , 1992, Mach. Learn..
[23] Elie Bienenstock,et al. Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.
[24] Yaser S. Abu-Mostafa,et al. A Method for Learning From Hints , 1992, NIPS.
[25] Richard S. Sutton,et al. Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta , 1992, AAAI.
[26] Sebastian Thrun,et al. Explanation-Based Neural Network Learning for Robot Control , 1992, NIPS.
[27] John R. Koza,et al. Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.
[28] Jonas Karlsson,et al. Learning Multiple Goal Behavior via Task Decomposition and Dynamic Policy Merging , 1993 .
[29] Lorien Y. Pratt,et al. Transferring previously learned back-propagation neural networks to new learning tasks , 1993 .
[30] Rich Caruana,et al. Multitask Learning: A Knowledge-Based Source of Inductive Bias , 1993, ICML.
[31] Sebastian Thrun,et al. Integrating Inductive Neural Network Learning and Explanation-Based Learning , 1993, IJCAI.
[32] Mark Ring. Two methods for hierarchy learning in reinforcement environments , 1993 .
[33] Luc De Raedt,et al. Multiple Predicate Learning , 1993, IJCAI.
[34] Dean A. Pomerleau,et al. Knowledge-Based Training of Artificial Neural Networks for Autonomous Robot Driving , 1993 .
[35] A. Waibel,et al. Multi-speaker/speaker-independent architectures for the multi-state time delay neural network , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[36] W. Ahn,et al. Psychological Studies of Explanation—Based Learning , 1993 .
[37] Leslie Pack Kaelbling,et al. Hierarchical Learning in Stochastic Domains: Preliminary Results , 1993, ICML.
[38] Bernard Widrow,et al. Neural networks: applications in industry, business and science , 1994, CACM.
[39] Umesh V. Vazirani,et al. An Introduction to Computational Learning Theory , 1994 .
[40] John R. Koza,et al. Genetic programming 2 - automatic discovery of reusable programs , 1994, Complex adaptive systems.
[41] Sebastian Thrun,et al. Finding Structure in Reinforcement Learning , 1994, NIPS.
[42] Tom M. Mitchell,et al. Using the Future to Sort Out the Present: Rankprop and Multitask Learning for Medical Risk Evaluation , 1995, NIPS.
[43] David Beymer,et al. Face recognition from one example view , 1995, Proceedings of IEEE International Conference on Computer Vision.
[44] Sebastian Thrun,et al. Explanation-based neural network learning a lifelong learning approach , 1995 .
[45] Carla E. Brodley. Recursive automatic algorithm selection for inductive learning , 1995 .
[46] Jonathan Baxter,et al. Learning internal representations , 1995, COLT '95.
[47] Andrew G. Barto,et al. Learning to Act Using Real-Time Dynamic Programming , 1995, Artif. Intell..
[48] Robert Tibshirani,et al. Discriminant Adaptive Nearest Neighbor Classification , 1995, IEEE Trans. Pattern Anal. Mach. Intell..
[49] Bartlett W. Mel. SEEMORE: a view-based approach to 3-D object recognition using multiple visual cues , 1996, Proceedings of 13th International Conference on Pattern Recognition.
[50] Sebastian Thrun,et al. Discovering Structure in Multiple Learning Tasks: The TC Algorithm , 1996, ICML.
[51] Rich Caruana,et al. Algorithms and Applications for Multitask Learning , 1996, ICML.
[52] Michael J. Pazzani,et al. Learning sets of related concepts: A shared task model , 1996 .
[53] Astro Teller,et al. Evolving programmers: the co-evolution of intelligent recombination operators , 1996 .
[54] Lorien Y. Pratt,et al. A Survey of Transfer Between Connectionist Networks , 1996, Connect. Sci..
[55] Jonathan Baxter,et al. The Canonical Distortion Measure for Vector Quantization and Function Approximation , 1997, ICML.
[56] R. Mooney,et al. A Multistrategy Approach to Theory Refinement , 1997 .
[57] Astro Teller,et al. PADO: a new learning architecture for object recognition , 1997 .
[58] Andrew G. Barto,et al. Reinforcement learning , 1998 .