Constructive learning: inducing grammars and neural networks

xviii

[1]  Rémi Gilleron,et al.  PAC Learning under Helpful Distributions , 1997, RAIRO Theor. Informatics Appl..

[2]  Pattie Maes,et al.  Agents that reduce work and information overload , 1994, CACM.

[3]  Rajesh Parekh,et al.  Constructive Neural Network Learning Algorithms for Multi-Category Pattern Classification , 1995 .

[4]  A CarpenterGail,et al.  The ART of Adaptive Pattern Recognition by a Self-Organizing Neural Network , 1988 .

[5]  Leslie G. Valiant,et al.  Cryptographic Limitations on Learning Boolean Formulae and Finite Automata , 1993, Machine Learning: From Theory to Applications.

[6]  Neil Burgess,et al.  A Constructive Algorithm that Converges for Real-Valued Input Patterns , 1994, Int. J. Neural Syst..

[7]  Rajesh Parekh,et al.  Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms , 1995 .

[8]  Umesh V. Vazirani,et al.  An Introduction to Computational Learning Theory , 1994 .

[9]  E. Capaldi,et al.  The organization of behavior. , 1992, Journal of applied behavior analysis.

[10]  Li-Min Fu,et al.  Knowledge-based connectionism for revising domain theories , 1993, IEEE Trans. Syst. Man Cybern..

[11]  Tom M. Mitchell,et al.  Generalization as Search , 2002 .

[12]  Sally A. Goldman,et al.  Teaching a Smarter Learner , 1996, J. Comput. Syst. Sci..

[13]  Dana Angluin,et al.  Learning Regular Sets from Queries and Counterexamples , 1987, Inf. Comput..

[14]  Allen Ginsberg,et al.  Theory Reduction, Theory Revision, and Retranslation , 1990, AAAI.

[15]  Pierre Dupont,et al.  Incremental regular inference , 1996, ICGI.

[16]  Raymond J. Mooney,et al.  Comparing Methods for Refining Certainty-Factor Rule-Bases , 1994, ICML.

[17]  Leonard Pitt,et al.  The minimum consistent DFA problem cannot be approximated within any polynomial , 1993, JACM.

[18]  Scott E. Fahlman,et al.  An empirical study of learning speed in back-propagation networks , 1988 .

[19]  Raymond J. Mooney,et al.  Theory Refinement Combining Analytical and Empirical Methods , 1994, Artif. Intell..

[20]  John W. Carr,et al.  A Solution of the Syntactical Induction-Inference Problem for Regular Languages , 1978, Comput. Lang..

[21]  Vasant Honavar,et al.  A Simple Randomized Quantization Algorithm for Neural Network Pattern Classifiers , 1996 .

[22]  Enrique Vidal,et al.  What Is the Search Space of the Regular Inference? , 1994, ICGI.

[23]  S. Dreyfus The numerical solution of variational problems , 1962 .

[24]  David Starer,et al.  Artificial Neural Nets , 1995 .

[25]  Rajesh Parekh,et al.  An Incremental Interactive Algorithm for Regular Grammar Inference , 1996, AAAI/IAAI, Vol. 2.

[26]  Jihoon Yang,et al.  MUpstart-a constructive neural network learning algorithm for multi-category pattern classification , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[27]  Jihoon Yang,et al.  Feature Subset Selection Using a Genetic Algorithm , 1998, IEEE Intell. Syst..

[28]  Stephen I. Gallant,et al.  Perceptron-based learning algorithms , 1990, IEEE Trans. Neural Networks.

[29]  Pat Langley,et al.  Elements of Machine Learning , 1995 .

[30]  Dana Angluin,et al.  A Note on the Number of Queries Needed to Identify Regular Languages , 1981, Inf. Control..

[31]  J. Oncina,et al.  INFERRING REGULAR LANGUAGES IN POLYNOMIAL UPDATED TIME , 1992 .

[32]  J. Feldman,et al.  A SURVEY OF RESULTS IN GRAMMATICAL INFERENCE , 1972 .

[33]  Russell Reed,et al.  Pruning algorithms-a survey , 1993, IEEE Trans. Neural Networks.

[34]  Pierre Dupont Utilisation et apprentissage de modeles de langage pour la reconnaissance de la parole continue , 1996 .

[35]  L.-M. Fu,et al.  Integration of neural heuristics into knowledge-based inference , 1989, International 1989 Joint Conference on Neural Networks.

[36]  David Carmel,et al.  Learning Models of Intelligent Agents , 1996, AAAI/IAAI, Vol. 1.

[37]  DANA ANGLUIN,et al.  On the Complexity of Minimum Inference of Regular Sets , 1978, Inf. Control..

[38]  Ronen Feldman,et al.  Bias-Driven Revision of Logical Domain Theories , 1993, J. Artif. Intell. Res..

[39]  Leslie G. Valiant,et al.  Cryptographic limitations on learning Boolean formulae and finite automata , 1994, JACM.

[40]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[41]  Rajesh Parekh,et al.  Constructive theory refinement in knowledge based neural networks , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[42]  Marcus Frean,et al.  The Upstart Algorithm: A Method for Constructing and Training Feedforward Neural Networks , 1990, Neural Computation.

[43]  Leslie G. Valiant,et al.  A theory of the learnable , 1984, STOC '84.

[44]  M. Golea,et al.  A Convergence Theorem for Sequential Learning in Two-Layer Perceptrons , 1990 .

[45]  Nils J. Nilsson,et al.  The Mathematical Foundations of Learning Machines , 1990 .

[46]  Ben J. A. Kröse,et al.  Learning from delayed rewards , 1995, Robotics Auton. Syst..

[47]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[48]  David W. Opitz,et al.  Dynamically adding symbolically meaningful nodes to knowledge-based neural networks , 1995, Knowl. Based Syst..

[49]  Rajesh Parekh,et al.  Comparison of performance of variants of single-layer perceptron algorithms on nonseparable data , 2000 .

[50]  Noam Chomsky,et al.  Three models for the description of language , 1956, IRE Trans. Inf. Theory.

[51]  Taylor L. Booth,et al.  Grammatical Inference: Introduction and Survey - Part I , 1975, IEEE Trans. Syst. Man Cybern..

[52]  Ming Li,et al.  Learning Simple Concept Under Simple Distributions , 1991, SIAM J. Comput..

[53]  Marvin Minsky,et al.  Perceptrons: An Introduction to Computational Geometry , 1969 .

[54]  Rajesh Parekh,et al.  Constructive Neural Network Learning Algorithms for Multi-Category Real-Valued Pattern Classification , 1997 .

[55]  Christos H. Papadimitriou,et al.  Elements of the Theory of Computation , 1997, SIGA.

[56]  Larry A. Rendell,et al.  Rerepresenting and Restructuring Domain Theories: A Constructive Induction Approach , 1994, J. Artif. Intell. Res..

[57]  Rajesh Parekh,et al.  Pruning strategies for constructive neural network learning algorithms , 1997 .

[58]  Eric B. Baum,et al.  Constructing Hidden Units Using Examples and Queries , 1990, NIPS.

[59]  E. Mark Gold,et al.  Complexity of Automaton Identification from Given Data , 1978, Inf. Control..

[60]  Taylor L. Booth,et al.  Grammatical Inference: Introduction and Survey-Part I , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[61]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[62]  J. C. Martin,et al.  Introduction to Languages and the Theory of Computation" 3rd Ed , 1991 .

[63]  Arthur E. Bryson,et al.  Applied Optimal Control , 1969 .

[64]  Vasant G Honavar,et al.  MTiling A Constructive Neural Network Learning Algorithm for Multi Category Pattern Classi cation , 1996 .

[65]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[66]  Rajesh Parekh,et al.  A Polynomial Time Incremental Algorithm for Regular Grammar Inference , 1997 .

[67]  Leonard Pitt,et al.  Reductions among prediction problems: on the difficulty of predicting automata , 1988, [1988] Proceedings. Structure in Complexity Theory Third Annual Conference.

[68]  Vasant Honavar,et al.  Generatlve Learning Structures and Processes for Connectionist Networks , 1993 .

[69]  Vasant Honavar,et al.  Toward learning systems that integrate multiple strategies and representations , 1994 .

[70]  David W. Opitz,et al.  Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies , 1997, J. Artif. Intell. Res..

[71]  Jerome A. Feldman,et al.  Learning automata from ordered examples , 1991, COLT '88.