Scott E. Fahlman

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Christian LebiereResearch Faculty, Psychology Department, Carnegie Mellon UniversityDirección de correo verificada de cmu.edu
Geoffrey HintonEmeritus Prof. Computer Science, University of TorontoDirección de correo verificada de cs.toronto.edu
David TouretzkyProfessor of Computer Science, Carnegie Mellon UniversityDirección de correo verificada de cs.cmu.edu
Terrence SejnowskiFrancis Crick Professor, Salk Institute, Distingished Professor, UC San DiegoDirección de correo verificada de salk.edu
Juan Carlos LópezProfessor of Computer Architecture, University of Castilla-La ManchaDirección de correo verificada de uclm.es
Francisco MoyaUniversity of Castilla-La Mancha, School of Industrial and Aerospace EngineeringDirección de correo verificada de uclm.es
Rahul SukthankarGoogle ResearchDirección de correo verificada de google.com
Shumeet BalujaGoogle, Inc.Dirección de correo verificada de google.com
Benjamin W. WahChinese University of Hong KongDirección de correo verificada de illinois.edu
Douglas FisherVanderbilt UniversityDirección de correo verificada de vanderbilt.edu
Tom MitchellFounders University Professor of Machine Learning, Carnegie Mellon UniversityDirección de correo verificada de cs.cmu.edu

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Scott E. Fahlman

Professor Emeritus, Language Technologies and Computer Science, Carnegie Mellon University

Dirección de correo verificada de cs.cmu.edu - Página principal

Artificial Intelligence Knowledge Representation and Reasoning


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
The cascade-correlation learning architecture S Fahlman, C Lebiere Advances in neural information processing systems 2, 1989	4670	1989
Faster learning variations of back propagation: An empirical study SE Fahlman Proceedings of the 1988 connectionist models summer school, 38-51, 1988	1821	1988
An empirical study of learning speed in back-propagation networks SE Fahlman Carnegie Mellon University, Computer Science Department, 1988	1446	1988
NETL: A system for representing and using real-world knowledge SE Fahlman American Journal of Computational Linguistics 6 (3-4), 1980	918	1980
A planning system for robot construction tasks SE Fahlman Artificial intelligence 5 (1), 1-49, 1974	359	1974
The recurrent cascade-correlation architecture S Fahlman Advances in neural information processing systems 3, 1990	342	1990
What can massively parallel architectures bring to AI? SE Fahlman Australian Joint Conference on Artificial Intelligence, 7-11, 1988	323	1988
Learning with limited numerical precision using the cascade-correlation algorithm M Hoehfeld, SE Fahlman Carnegie-Mellon University. Department of Computer Science, 1991	307	1991
Connectionist architectures for artificial intelligence SE Fahlman, GE Hinton Computer 20 (01), 100-109, 1987	257	1987
Method for transforming message containing sensitive information SE Fahlman, R Sukthankar, A Brusseau US Patent 5,960,080, 1999	179	1999
Advances in neural information processing systems SE Fahlman, C Lebiere, D Touretzky	111	1990
A system for representing and using real-world knowledge SE Fahlman	101	1977
Cancellation in a Parallel Semantic Network. SE Fahlman, DS Touretzky, W van Roggen Ijcai, 257-263, 1981	89	1981
Reducing network depth in the cascade-correlation learning architecture S Baluja, SE Fahlman Carnegie-Mellon University. Department of Computer Science, 1994	86	1994
Proceedings of the national conference on artificial intelligence SE Fahlman, GE Hinton, TJ Sejnowski	85	1983
The cascade-correlation learning algorithm SE Fahlman Neural Information Proccesing Systems, 524-532, 1990	84	1990
NETL: A system for representing real-world knowledge SE Fahlman MIT Press, 1979	72	1979
Representing implicit knowledge SE Fahlman Parallel models of associative memory, 171-185, 2014	71	2014
Smiley lore:- SE Fahlman Retrieved October 6, 2002, 2002	71	2002
Marker-passing inference in the scone knowledge-base system SE Fahlman International Conference on Knowledge Science, Engineering and Management …, 2006	70	2006

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