Knowledge selection in category learning

[1]  C. Hartshorne,et al.  Collected Papers of Charles Sanders Peirce , 1935, Nature.

[2]  S. Tipper,et al.  Quarterly Journal of Experimental Psychology , 1948, Nature.

[3]  C. I. Hovland,et al.  The Influence of Source Credibility on Communication Effectiveness , 1951 .

[4]  P. Wason On the Failure to Eliminate Hypotheses in a Conceptual Task , 1960 .

[5]  H. Raiffa,et al.  Applied Statistical Decision Theory. , 1961 .

[6]  Howard Raiffa,et al.  Applied Statistical Decision Theory. , 1961 .

[7]  Vladimir Vapnik,et al.  Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities , 1971 .

[8]  Douglas L. Medin,et al.  Context theory of classification learning. , 1978 .

[9]  Lynn Hasher,et al.  Is memory schematic , 1983 .

[10]  D. Medin,et al.  The role of theories in conceptual coherence. , 1985, Psychological review.

[11]  D. Medin,et al.  Family resemblance, conceptual cohesiveness, and category construction , 1987, Cognitive Psychology.

[12]  N. Sharkey,et al.  Advances in cognitive science , 1988 .

[13]  Gregory Ashby,et al.  Decision rules in the perception and categorization of multidimensional stimuli. , 1988, Journal of experimental psychology. Learning, memory, and cognition.

[14]  G. Bower,et al.  From conditioning to category learning: an adaptive network model. , 1988, Journal of experimental psychology. General.

[15]  Gregory L. Murphy,et al.  Feature correlations in conceptual representations , 1989 .

[16]  E. Markman Categorization and naming in children , 1989 .

[17]  Michael C. Mozer,et al.  Using Relevance to Reduce Network Size Automatically , 1989 .

[18]  Michael I. Jordan,et al.  Task Decomposition Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks , 1990, Cogn. Sci..

[19]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[20]  F. Keil Concepts, Kinds, and Cognitive Development , 1989 .

[21]  E. Heit Categorization using chains of examples , 1992, Cognitive Psychology.

[22]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

[23]  Ryszard S. Michalski,et al.  Categories and Concepts: Theoretical Views and Inductive Data Analysis , 1993 .

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

[25]  R. Mooney Integrating Theory and Data in Category Learning , 1993 .

[26]  M. McDaniel,et al.  Incorporating prior biases innetwork models of conceptual rule learning , 1993, Memory & cognition.

[27]  C. Lee Giles,et al.  Extraction, Insertion and Refinement of Symbolic Rules in Dynamically Driven Recurrent Neural Networks , 1993 .

[28]  Gregory L. Murphy,et al.  Theories and concept formation. , 1993 .

[29]  Yaser S. Abu-Mostafa,et al.  Hints and the VC Dimension , 1993, Neural Computation.

[30]  Paul D. Allopenna,et al.  The locus of knowledge effects in concept learning. , 1994, Journal of experimental psychology. Learning, memory, and cognition.

[31]  C. Hulme,et al.  Cell suicide in the developing nervous system: a functional neural network model. , 1994, Brain research. Cognitive brain research.

[32]  T. B. Ward Structured Imagination: the Role of Category Structure in Exemplar Generation , 1994, Cognitive Psychology.

[33]  Douglas L. Medin,et al.  On the Interaction of Theory and Data in Concept Learning , 1994, Cogn. Sci..

[34]  R. Nosofsky,et al.  Rule-plus-exception model of classification learning. , 1994, Psychological review.

[35]  D Kelemen,et al.  Domain-specific knowledge in simple categorization tasks , 1994, Psychonomic bulletin & review.

[36]  E. Heit,et al.  Models of the effects of prior knowledge on category learning. , 1994, Journal of experimental psychology. Learning, memory, and cognition.

[37]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[38]  E. Wisniewski,et al.  Prior knowledge and functionally relevant features in concept learning. , 1995, Journal of experimental psychology. Learning, memory, and cognition.

[39]  C. Lee Giles,et al.  Constructive learning of recurrent neural networks: limitations of recurrent cascade correlation and a simple solution , 1995, IEEE Trans. Neural Networks.

[40]  R. Shillcock,et al.  Proceedings of the Seventeenth Annual Conference of the Cognitive Science Society , 1995 .

[41]  B. Hayes,et al.  Similarity-based and knowledge-based processes in category learning , 1995 .

[42]  Robert A. Jacobs,et al.  Methods For Combining Experts' Probability Assessments , 1995, Neural Computation.

[43]  Giovanni Soda,et al.  Recurrent neural networks and prior knowledge for sequence processing: a constrained nondeterministic approach , 1995, Knowl. Based Syst..

[44]  T. Shultz,et al.  Generative connectionist networks and constructivist cognitive development , 1996 .

[45]  Robert A. Jacobs,et al.  Nature, nurture, and the development of functional specializations: A computational approach , 1997 .

[46]  Lutz Prechelt,et al.  Investigation of the CasCor Family of Learning Algorithms , 1997, Neural Networks.

[47]  Gregory Ashby,et al.  A neuropsychological theory of multiple systems in category learning. , 1998, Psychological review.

[48]  Evan Heit,et al.  A Bayesian Analysis of Some Forms of Inductive Reasoning , 1998 .

[49]  J. Kruschke,et al.  Rules and exemplars in category learning. , 1998, Journal of experimental psychology. General.

[50]  Robert L. Goldstone,et al.  The development of features in object concepts , 1998, Behavioral and Brain Sciences.

[51]  E Heit Influences of prior knowledge on selective weighting of category members. , 1998, Journal of experimental psychology. Learning, memory, and cognition.

[52]  G. Murphy,et al.  What is learned in knowledge-related categories? Evidence from typicality and feature frequency judgments , 1999, Memory & cognition.

[53]  N. Chater,et al.  Rational models of cognition , 1998 .

[54]  E. Heit Belief Revision in Models of Category Learning , 2000 .