 |
 | ftp://synapse.cs.byu.edu/pub/papers/vanhorn_4.ps, 19940926
Robust Trainability of Single Neurons Klaus-U. H offgen, Hans-U. Simon Lehrstuhl Informatik II Universit at Dortmund D-4600 Dortmund 50 hoeffgen,simon@nereus.informatik.uni-dortmund.de Kevin S. Van Horn Computer Science Department Brigham Young University Provo, UT 84602 kevin@bert.cs.byu.edu June 3, |
| ftp://synapse.cs.byu.edu/pub/papers/vanhorn_3.ps, 19940926
The Minimum Feature Set Problem Kevin S. Van Horn and Tony Martinez Computer Science Department Brigham Young University Provo, UT This paper appeared in Neural Networks 7 (1994), no. 3, pp. 491{494. Minimum Feature Set / 1 The Minimum Feature Set Problem Kevin S. Van Horn and Tony Martinez Computer |
| ftp://synapse.cs.byu.edu/pub/papers/vanhorn_5.ps, 19940926
Extending Occam's Razor Kevin S. Van Horn & Tony R. Martinez 3361 TMCB Computer Science Department Brigham Young University Provo, UT 84602 email: kevin@bert.cs.byu.edu, martinez@cs.byu.edu This paper will appear in the Proceedings of the Third Golden West International Conference on Intelligent |
| ftp://synapse.cs.byu.edu/pub/papers/vanhorn_1.ps, 19940926
The BBG Rule Induction Algorithm Kevin S. Van Horn and Tony R. Martinez Computer Science Department, Brigham Young University Provo, UT 84602 U.S.A. This paper appeared in Proceedings of the 6th Australian Joint Conference on Artificial Intelligence, Melbourne, Australia, 17 Nov. 1993, pp. 348{355. The |
| ftp://synapse.cs.byu.edu/pub/papers/vanhorn_2.ps, 19940926
The Design and Evaluation of a Rule Induction Algorithm Kevin S. Van Horn and Tony R. Martinez Computer Science Department Brigham Young University Provo, UT 84602 email: vanhorn@bert.cs.byu.edu, martinez@cs.byu.edu technical report BYU-CS-93-11 June 1993 Keywords: machine learning, rule induction, |
| ftp://synapse.cs.byu.edu/pub/papers/cgc_93a.ps, 19940926
Using Precepts to Augment Training Set Learning1 Christophe Giraud-Carrier and Tony Martinez Department of Computer Science, Brigham Young University, Provo, UT 84602 |
| ftp://synapse.cs.byu.edu/pub/papers/cgc_95b.ps, 19950711
AA1 : A DYNAMIC INCREMENTAL NETWORK THAT LEARNS BY DISCRIMINATION Christophe Giraud-Carrier Tony Martinez Department of Computer Science Department of Computer Science University of Bristol Brigham Young University Bristol, BS8 1TR, U.K. Provo, UT 84602, U.S.A. |
| ftp://synapse.cs.byu.edu/pub/papers/cgc_94c.ps, 19950711
AN EFFICIENT METRIC FOR HETEROGENEOUS INDUCTIVE LEARNING APPLICATIONS IN THE ATTRIBUTE-VALUE LANGUAGE1 Christophe Giraud-Carrier and Tony Martinez Brigham Young University, Department of Computer Science, Provo, UT 84602 |
| ftp://synapse.cs.byu.edu/pub/papers/cgc_94b.ps, 19950711
AN INCREMENTAL LEARNING MODEL FOR COMMONSENSE REASONING Christophe Giraud-Carrier Department of Computer Science Brigham Young University Provo, UT 84602 Tony Martinez Department of Computer Science Brigham Young University Provo, UT 84602 |
| ftp://synapse.cs.byu.edu/pub/papers/cgc_94a.ps, 19950711
SEVEN DESIRABLE PROPERTIES FOR ARTIFICIAL LEARNING SYSTEMS Christophe Giraud-Carrier and Tony Martinez Brigham Young University, Department of Computer Science, Provo, UT 84602 cgc@axon.cs.byu.edu, martinez@cs.byu.edu |
| ftp://synapse.cs.byu.edu/pub/papers/mcdonald_91.ps, 19950712
In 4th International Symposium on Artificial Intelligence, pp. 371-377, 1991. A Connectionist Method for Adaptive Real-Time Network Routing Kelly C. McDonald Tony R. Martinez Douglas M. Campbell Department of Computer Science Brigham Young University, Provo, Utah 84602 |
| ftp://synapse.cs.byu.edu/pub/papers/martinez_88a.ps, 19950712
In Neural Networks, vol. 1, S1, p.552, 1988. ON THE EXPEDIENT USE OF NEURAL NETWORKS. Tony Martinez. Computer Science Dept., 230 TMCB, Brigham Young University, Provo, Utah 84602 USA Computational machines do deterministic mappings of inputs to outputs. Current implemented and proposed machines include |
| ftp://synapse.cs.byu.edu/pub/papers/martinez_89a.ps, 19950712
In Proceedings of the IEEE Symposium on Parallel and Distributed Computing, pp. 308-315, 1989. On the Pseudo Multilayer Learning of Backpropagation Tony Martinez and Michael Lindsey Computer Science Dept., Brigham Young University, Provo, Utah 84602 |
| ftp://synapse.cs.byu.edu/pub/papers/martinez_91a.ps, 19950712
In Journal of Parallel and Distributed Computing, vol. 11, No. 4, pp. 303-313, 1991. A Self-Adjusting Dynamic Logic Module Tony R. Martinez and Douglas M. Campbell Computer Science Department Brigham Young University Provo, Utah 84602 |
| ftp://synapse.cs.byu.edu/pub/papers/bertelsen_94.ps, 19950712
In FLAIRS'94 Florida Artificial Intelligence Research Symposium, pp. 122-125, 1994. Extending ID3 Through Discretization of Continuous Inputs Rick Bertelsen Tony R. Martinez Computer Science Department, Brigham Young University, Provo, Utah 84602 e-mail: rick@axon.cs.byu.edu, martinez@cs.byu.edu |
| ftp://synapse.cs.byu.edu/pub/papers/hughes_th.ps, 19950712
Prioritized Rule Systems A Thesis Presented to the Department of Computer Science Brigham Young University In Partial Fulfillment of the Requirements for the Degree Master of Science Brent W. Hughes 1989 by Brent W. Hughes November 1989 ii This thesis, by Brent W. Hughes, is accepted in its present form |
| ftp://synapse.cs.byu.edu/pub/papers/kemsley_92.ps, 19950712
In International Journal of Neural Networks: Research and Applications, vol. 2, No. 2/3/4, pp.123-133, 1992. A Survey of Neural Network Research and Fielded Applications David H. Kemsley Electronics Engineering Technology Department Tony R. Martinez Douglas M. Campbell Computer Science Department, |
| ftp://synapse.cs.byu.edu/pub/papers/bertelsen_th.ps, 19950712
i Automatic Feature Extraction in Machine Learning A Thesis Presented to the Department of Computer Science Brigham Young University In Partial Fulfillment of the Requirements for the Degree Master of Science Rick Bertelsen August 1994 |
| ftp://synapse.cs.byu.edu/pub/papers/martinez_94a.ps, 19950713
In Journal of Artificial Neural Networks, vol. 1, no. 3, pp. 403-429, 1994. Priority ASOCS Tony R. Martinez Douglas M. Campbell Brent W. Hughes Computer Science Department Brigham Young University Provo, Utah 84602 |
| ftp://synapse.cs.byu.edu/pub/papers/martinez_90c.ps, 19950713
In Progress in Neural Networks, vol. 1, Ch. 5, pp. 105-126, O. Omidvar (ed), Ablex Publishing, 1990. Adaptive Self-Organizing Concurrent Systems Tony R. Martinez Computer Science Dept. Brigham Young University 1. Introduction Current technology trends, demanding applications, and limitations of |
| ftp://synapse.cs.byu.edu/pub/papers/martinez_91b.ps, 19950713
In IEEE Systems, Man, and Cybernetics, vol. 21, No. 5, pp.1231-1238, 1991. A Self-Organizing Binary Decision Tree For Incrementally Defined Rule Based Systems Tony R. Martinez and Douglas M. Campbell Computer Science Department Brigham Young University Provo, Utah 84602 |
| ftp://synapse.cs.byu.edu/pub/papers/barker_93a.ps, 19950713
In AI'93 Australian Joint Conference on Artificial Intelligence, pp. 142-149, 1994. GENERALIZATION BY CONTROLLED INTERSECTION OF EXAMPLES Cory Barker and Tony Martinez Computer Science Department, Brigham Young University, Provo, Utah 84602 cory@axon.cs.byu.edu, martinez@cs.byu.edu |
| ftp://synapse.cs.byu.edu/pub/papers/martinez_89b.ps, 19950713
In Proceedings of the IASTED International Symposium on Expert Systems and Neural Networks, pp. 41-44, 1989. Neural Network Applicability: Classifying the Problem Space Tony Martinez Professor, Computer Science Dept. 230 TMCB, Brigham Young University Provo, Utah 84602 |
| ftp://synapse.cs.byu.edu/pub/papers/martinez_90b.ps, 19950713
In Proceedings of the International Symposium on Circuits and Systems, pp. 706-709, 1990. CONSISTENCY AND GENERALIZATION IN INCREMENTALLY TRAINED CONNECTIONIST NETWORKS Tony Martinez Computer Science Dept., Brigham Young University Provo, Utah 84602 |
| ftp://synapse.cs.byu.edu/pub/papers/martinez_93c.ps, 19950713
In IWANNT'93 International Workshop on Applications of Neural Networks to Telecommuniations, pp. 183-187, 1993. A Learning Model for Adaptive Network Routing Tony R. Martinez and George L. Rudolph Department of Computer Science Brigham Young University, Provo, Utah 84602 martinez@cs.byu.edu |
| ftp://synapse.cs.byu.edu/pub/papers/barker_94b.ps, 19950713
In Proceedings of the 7th International Symposium on Artificial Intelligence, pp. 142-149, 1994. GENERALIZATION BY CONTROLLED EXPANSION OF EXAMPLES Cory Barker Computer Science Department Brigham Young University Provo, Utah 84602 Tony Martinez Computer Science Department Brigham Young University Provo, |
| ftp://synapse.cs.byu.edu/pub/papers/martinez_88b.ps, 19950713
In Proceedings of the 1988 IEEE Systems Man and Cybernetics Conference, pp. 681-684, 1988. Digital Neural Networks Tony R. Martinez Computer Science Dept., 230 TMCB Brigham Young University, Provo, Utah 84602 To Appear in the Proceedings of the 1988 IEEE International Conference on System Man and |
| ftp://synapse.cs.byu.edu/pub/papers/barker_93c.ps, 19950713
In WCNN'93 World Congress on Neural Networks, vol. III, pp. 376-380, 1993. GS: A Network that Learns Important Features Cory Barker and Tony Martinez Computer Science Department, Brigham Young University, Provo, Utah 84602 |
| ftp://synapse.cs.byu.edu/pub/papers/barker_93b.ps, 19950713
In Proceedings of ICANN'93 International Conference on Artificial Neural Networks, 1993. Learning and Generalization Controlled by Contradiction Cory Barker and Tony Martinez Computer Science Department, Brigham Young University, Provo, Utah 84602 SG (Specific to General) is a network that learns from a |
| ftp://synapse.cs.byu.edu/pub/papers/martinez_93b.ps, 19950713
In ANNES'93 Conference on Artificial Neural Networks and Expert Systems, pp. 216-219, 1993. Towards a General Distributed Platform for Learning and Generalization Tony R. Martinez Brent W. Hughes Computer Science Department, Brigham Young University, Provo, Utah 84602 |
| ftp://synapse.cs.byu.edu/pub/papers/barker_95a.ps, 19950713
In Intelligent Systems, E. A. Yfantis (ed), vol. 1, pp. 717-625, Kluwer Academic Publishers, 1995. Efficient Construction of Networks for Learned Representations with General to Specific Relationships Cory Barker and Tony Martinez Brigham Young University, Provo, Utah 84602 cory@axon.cs.byu.edu, |
| ftp://synapse.cs.byu.edu/pub/papers/martinez_87a.ps, 19950713
In Proceedings of the 1987 Systems Man and Cybernetics Conference, pp. 290-296, 1987. Models of Parallel Adaptive Logic Tony Martinez Brigham Young University |
| ftp://synapse.cs.byu.edu/pub/papers/martinez_90a.ps, 19950713
In Modeling the Innovation: Communications, Automation and Information Systems, Carnavale, et.al. (eds), NorthHolland, pp. 481-488, (IFIP International Conference), 1990. Smart Memory: The Memory Processor Model Tony R. Martinez Computer Science Department, Brigham Young University Provo, UT, 84602, USA |
| ftp://synapse.cs.byu.edu/pub/papers/barker_94a.ps, 19950713
In IEEE Transactions on Systems, Man, and Cybernetics, vol. 24, No. 3, pp. 503-510, 1994. Proof of Correctness for ASOCS AA3 Networks1 Cory Barker and Tony R. Martinez Computer Science Department, Brigham Young University |
| ftp://synapse.cs.byu.edu/pub/papers/martinez_91c.ps, 19950713
In Proceedings of the 2nd Government Neural Network Workshop, 1991. ASOCS: Towards Bridging Neural Network and Artificial Intelligence Learning Tony Martinez Computer Science Department, Brigham Young University 801-378-6464, E-mail:martinez@cs.byu.edu Presented at 2nd Government Neural Network |
| ftp://synapse.cs.byu.edu/pub/papers/martinez_88c.ps, 19950713
Presented at 2nd IEEE International Conference on Neural Networks, 1988. ASOCS: A Multilayered Connectionist Network with Guaranteed Learning of Arbitrary Mappings Tony R. Martinez Computer Science Dept., 230 TMCB Brigham Young University, Provo, Utah 84602 |
| ftp://synapse.cs.byu.edu/pub/papers/martinez_93a.ps, 19950713
In WCNN'93 World Congress on Neural Networks, vol. I, pp. 613-616, 1993. A Generalizing Adaptive Discriminant Network Tony Martinez, Cory Barker and Christophe Giraud-Carrier Computer Science Department, Brigham Young University, Provo, Utah 84602 To appear in Proceedings of the 1993 World Congress on |
| ftp://synapse.cs.byu.edu/pub/papers/martinez_90d.ps, 19950713
In Future Generation Computing Systems, vol. 6, No. 1, pp.26-58, 1990. Smart Memory Architecture and Methods Tony R. Martinez Computer Science Department, Brigham Young University |
| ftp://synapse.cs.byu.edu/pub/papers/rudolph_95a.ps, 19950714
To appear in Journal of Artificial Neural Networks, 1995. This research is funded in part by grants from Novell Inc. and Word Perfect Corp. An Efficient Transformation for Implementing Two-layer Feedforward Neural Networks George L. Rudolph Tony R. Martinez Computer Science Department Brigham Young |
| ftp://synapse.cs.byu.edu/pub/papers/rudolph_91a.ps, 19950714
In Artificial Neural Networks, Kohonen, et. al. (eds), Elsevier Science Publishers, pp. 729-734, 1991. AN EFFICIENT STATIC TOPOLOGY FOR MODELING ASOCS George L. Rudolph and Tony R. Martinez Department of Computer Science Brigham Young University Provo, Utah, USA, 84602 ASOCS (Adaptive Self-Organizing |
| ftp://synapse.cs.byu.edu/pub/papers/rudolph_94a.ps, 19950714
In International Journal on Artificial Intelligence Tools, vol. 3, No. 3, pp. 417-427, 1994. LOCATION-INDEPENDENT TRANSFORMATIONS: A GENERAL STRATEGY FOR IMPLEMENTING NEURAL NETWORKS George L. Rudolph and Tony R. Martinez Computer Science Department Brigham Young University Provo, Utah 84602 e-mail: |
| ftp://synapse.cs.byu.edu/pub/papers/rudolph_89a.ps, 19950714
In Proceedings of the IASTED International Symposium on Expert Systems and Neural Networks, pp. 12-15, 1989. DNA: A New ASOCS Model With Improved Implementation Potential George L. Rudolph and Tony R. Martinez Department of Computer Science Brigham Young University Provo, Utah 84602 |
| ftp://synapse.cs.byu.edu/pub/papers/barker_diss.ps, 19950714
Eclectic Machine Learning A Dissertation Presented to the Department of Computer Science Brigham Young University In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy Cory Barker 1994 by Cory Barker February 1994 This dissertation by Cory Barker is accepted in its present form |
| ftp://synapse.cs.byu.edu/pub/papers/rudolph_95d.ps, 19950714
Intelligent Systems, E. A. Yfantis (ed.), Vol. 1, pp. 637-645, Kluwer Academic Publishers, 1995. This research is funded by grants from Novell Inc. and Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING NEURAL NETWORKS WITH LOCALIST PROPERTIES George L. Rudolph Tony R. Martinez Computer Science |
| ftp://synapse.cs.byu.edu/pub/papers/rudolph_95c.ps, 19950714
In proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 41-44, 1995. This research is funded by grants from Novell Inc. and Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING EFFICIENT DYNAMIC BACKPROPAGATION NEURAL NETWORKS George L. Rudolph and Tony |
| ftp://synapse.cs.byu.edu/pub/papers/cgc_95c.ps, 19950714
In Journal of Parallel and Distributed Computing, vol. 26, pp. 125-131, 1995. Analysis of the Convergence and Generalization of AA1 Christophe Giraud-Carrier and Tony Martinez Department of Computer Science, Brigham Young University |
| ftp://synapse.cs.byu.edu/pub/papers/andersen_95a.ps, 19950718
Learning and Generalization with Bounded Order Rule Sets A Thesis Presented to the Department of Computer Science Brigham Young University In Partial Fulfillment of the Requirement for the Degree Master of Science by Timothy L. Andersen April 1995 ii Acceptance Page This thesis, by Tim Andersen, is |
| ftp://synapse.cs.byu.edu/pub/papers/stout_mcm.ps, 19950726
A Multi-Chip Module Implementation of a Neural Network Matthew G. Stout George L. Rudolph Linton G. Salmon Tony R. Martinez Department of Department of Computer Science Electrical and Computer Engineering Brigham Young University Brigham Young University Provo, UT 84602 Provo, UT 84602 |
| ftp://synapse.cs.byu.edu/pub/papers/wilson.annes93.mult.ps, 19950726
Proceedings of the First New Zealand International Conference on Artificial Neural Networks and Expert Systems (ANNES), pp. 54-57, November 1993. The Importance of Using Multiple Styles of Generalization* D. Randall Wilson e-mail: randy@axon.cs.byu.edu Tony R. Martinez e-mail: martinez@cs.byu.edu |
| ftp://synapse.cs.byu.edu/pub/papers/stout_icpr.ps, 19950726
A VLSI Implementation of a Parallel, Self-Organizing Learning Model Matthew G. Stout George L. Rudolph Linton G. Salmon Tony R. Martinez Department of Department of Computer Science Electrical and Computer Engineering Brigham Young University Brigham Young University Provo, UT 84602 Provo, UT 84602 |
| ftp://synapse.cs.byu.edu/pub/papers/hart_th.ps, 19950726
Extending ASOCS to Training-Set-Style Data A Thesis Presented to the Department of Computer Science Brigham Young University In Partial Fulfillment of the Requirements for the Degree Master of Science by Edward F. Hart August 1992 i This thesis, by Edward F. Hart is accepted in its present form by the |
| ftp://synapse.cs.byu.edu/pub/papers/wilson.ai93.proto.ps, 19950726
Proceedings of the 6th Australian Joint Conference on Artificial Intelligence (AI 93), pp. 356-361, Nov. 1993. The Potential of Prototype Styles of Generalization D. Randall Wilson Tony R. Martinez Computer Science Department, Brigham Young University, Provo, Utah 84602 e-mail: randy@axon.cs.byu.edu, |
| ftp://synapse.cs.byu.edu/pub/papers/rudolph_diss.ps, 19950726
Location-Independent Neural Network Models A Dissertation Presented to the Department of Computer Science Brigham Young University In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy George L. Rudolph 1995 by George Rudolph July 26, 1995 ii This Dissertation by George Rudolph |
| ftp://synapse.cs.byu.edu/pub/papers/rudolph_95b.ps, 19950727
In Neural Parallel and Scientific Computations, vol. 3, no. 2, pp. 173-188, 1995. This research is funded by grants from Novell Inc. and Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING LOCALIST NEURAL NETWORKS George L. Rudolph and Tony R. Martinez Brigham Young University, Computer Science |
| ftp://synapse.cs.byu.edu/pub/papers/rudolph_th.ps, 19950727
1 A Location-Independent ASOCS Model A Thesis Presented to the Department of Computer Science Brigham Young University In Partial Fulfillment of the Requirements for the Degree Master of Science by George Rudolph June 1991 2 This thesis by George Rudolph is accepted in its present form by the Department |
| ftp://synapse.cs.byu.edu/pub/papers/wilson.thesis94.ps, 19950727
Prototype Styles of Generalization A Thesis Presented to the Department of Computer Science Brigham Young University In Partial Fulfillment of the Requirement for the Degree Master of Science by D. Randall Wilson August 1994 ii This thesis, by D. Randall Wilson is accepted in its present form by the |
| ftp://synapse.cs.byu.edu/pub/papers/cgc_diss.ps, 19950731
1 On Integrating Inductive Learning with Prior Knowledge and Reasoning A Dissertation Presented to the Department of Computer Science Brigham Young University in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy Christophe G. Giraud-Carrier 1994 by Christophe G. Giraud-Carrier |
| ftp://synapse.cs.byu.edu/pub/papers/cgc_th.ps, 19950803
A Precept-Driven Learning Algorithm A Thesis Presented to the Department of Computer Science Brigham Young University In Partial Fulfillment of the Requirements for the Degree Master of Science by Christophe G. Giraud-Carrier April 1993 ii This thesis by Christophe Giraud-Carrier is accepted in its |
| ftp://synapse.cs.byu.edu/pub/papers/ventura.thesis.ps, 19950818
On Discretization as a Preprocessing Step For Supervised Learning Models A Thesis Presented to the Department of Computer Science Brigham Young University In Partial Fulfillment of the Requirements for the Degree Master of Science by Dan Ventura April 1995 ii This thesis, by Dan Ventura, is accepted in |
| ftp://synapse.cs.byu.edu/pub/papers/ventura.ep96.ps, 19950928
Submitted to the Fifth Annual Conference on Evolutionary Programming 1996 Using Training Set Evolution in Perfect and Imperfect Systems Dan Ventura Tim Andersen Tony R. Martinez Computer Science Department, Brigham Young University, Provo, Utah 84602 e-mail: dan@axon.cs.byu.edu, tim@axon.cs.byu.edu, |
| ftp://synapse.cs.byu.edu/pub/papers/ventura.icannga95.ps, 19950928
Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 468-71, 1995 USING EVOLUTIONARY COMPUTATION TO GENERATE TRAINING SET DATA FOR NEURAL NETWORKS Dan Ventura Tim Andersen Tony R. Martinez Provo, Utah 84602Computer Science Department, Brigham Young |
| ftp://synapse.cs.byu.edu/pub/papers/ventura.jann96.ps, 19950928
Submitted to the Journal of Artificial Neural Networks special issue on Optimization, 1996 Optimization by Combining Evolutionary Computation With Neural Networks Dan Ventura Tim Andersen Tony R. Martinez Brigham Young University A method of optimization using a neural network with an evolutionary |
| ftp://synapse.cs.byu.edu/pub/papers/ventura.isninc95.ps, 19950928
Proceedings of the International Symposium on Neuroinformatics and Neurocomputers, pp. 238-45, 1995 Using Multiple Statistical Prototypes to Classify Continuously Valued Data Dan Ventura and Tony R. Martinez Computer Science Department, Brigham Young University, Provo, Utah 84602 e-mail: |
| ftp://synapse.cs.byu.edu/pub/papers/ventura.flairs94.ps, 19950928
Proceedings of the Seventh Florida Artificial Intelligence Research Symposium, pp. 117-21, 1994 BRACE: A Paradigm For the Discretization of Continuously Valued Data Dan Ventura Tony R. Martinez Computer Science Department, Brigham Young University, Provo, Utah 84602 e-mail: dan@axon.cs.byu.edu, |
| ftp://synapse.cs.byu.edu/pub/papers/ventura.iscis95.ps, 19951107
Proceedings of the Tenth International Symposium on Computer and Information Sciences, pp. 443-450, 1995 An Empirical Comparison of Discretization Methods Dan Ventura and Tony R. Martinez Computer Science Department, Brigham Young University, Provo, Utah 84602 e-mail: dan@axon.cs.byu.edu, |
| ftp://synapse.cs.byu.edu/pub/papers/andersen_95d.ps, 19951115
Proceedings of the 2nd International Symposium on Neuroinformatics and Neurocomputers, pp. 77-84, 1995. A Provably Convergent Dynamic Training Method for Multi-layer Perceptron Networks Tim L. Andersen and Tony R. Martinez Computer Science Department, Brigham Young University, Provo, Utah 84602 e-mail: |
| ftp://synapse.cs.byu.edu/pub/papers/andersen_95c.ps, 19951115
Proceedings of the 10th International Symposium on Computer and Information Sciences, pp. 411-418, 1995. NP-Completeness of Minimum Rule Sets Tim L. Andersen and Tony R. Martinez Computer Science Department, Brigham Young University, Provo, Utah 84602 e-mail: tim@axon.cs.byu.edu, martinez@cs.byu.edu |
| ftp://synapse.cs.byu.edu/pub/papers/andersen_93a.ps, 19951115
This research was partially funded by grants from WordPerfect Corporation and Novell Inc. Proceedings of the AI '93 Australian Joint Conference on Artificial Intelligence, pp. 450, 1993. Learning and Generalization with Bounded Order Critical Feature Sets Timothy L. Andersen and Tony R. Martinez |
| ftp://synapse.cs.byu.edu/pub/papers/andersen_95b.ps, 19951115
Proceedings of the 10th International Symposium on Computer and Information Sciences, pp. 419-426, 1995. Learning and Generalization with Bounded Order Rule Sets Tim L. Andersen and Tony R. Martinez Computer Science Department, Brigham Young University, Provo, Utah 84602 e-mail: tim@axon.cs.byu.edu, |
| ftp://synapse.cs.byu.edu/pub/papers/wilson.aie96.ivdm.ps, 19960513
To appear, International Conference on Artificial Intelligence, Expert Systems and Neural Networks (AIE 96), August 1996. Value Difference Metrics for Continuously Valued Attributes D. Randall Wilson, Tony R. Martinez e-mail: randy@axon.cs.byu.edu, martinez@cs.byu.edu Computer Science Department, |
| ftp://synapse.cs.byu.edu/pub/papers/wilson.aie96.gibl.ps, 19960513
To appear, International Conference on Artificial Intelligence, Expert Systems and Neural Networks (AIE 96), August 1996. Instance-Based Learning with Genetically Derived Attribute Weights D. Randall Wilson, Tony R. Martinez e-mail: randy@axon.cs.byu.edu, martinez@cs.byu.edu Computer Science Department, |
| ftp://synapse.cs.byu.edu/pub/papers/wilson.icnn96.hrbf.ps, 19960612
Proceedings of the International Conference on Neural Networks (ICNN 96), vol. 2, pp. 1263-1267, June 1996. z x2 x3 h1 h2 h3 h4 c1 c2 x: Hidden nodes Class nodes Input nodes x1 Decision node Figure 1. Radial Basis Function Network. Heterogeneous Radial Basis Function Networks D. Randall Wilson, Tony R. |
| ftp://synapse.cs.byu.edu/pub/papers/wilson.jair96.hvdm.ps, 19960815
Improved Heterogeneous Distance Functions D. Randall Wilson, Tony R. Martinez e-mail: randy@axon.cs.byu.edu, martinez@cs.byu.edu Computer Science Department, Brigham Young University, Provo, UT 84602, U.S.A. |
| ftp://synapse.cs.byu.edu/pub/papers/ventura.iasted96.ps, 19960826
Proceedings of the IASTED International Conference on Expert Systems, Artificial Intelligence, and Neural Networks, pp. 44-47, 1996 Concerning a General Framework for the Development of Intelligent Systems Dan Ventura Tony R. Martinez Computer Science Department, Brigham Young University, Provo, Utah |
| ftp://synapse.cs.byu.edu/pub/papers/ventura.icnn96.ps, 19960826
Proceedings of the International Conference on Neural Networks, pp. 524-528, 1996 Robust Optimization Using Training Set Evolution Dan Ventura Tony R. Martinez Computer Science Department, Brigham Young University, Provo, Utah 84602 e-mail: dan@axon.cs.byu.edu, martinez@cs.byu.edu |
| ftp://synapse.cs.byu.edu/pub/papers/ventura.wcnn96.ps, 19960920
Proceedings of the World Congress on Neural Networks, pp. 1091-5, 1996 A General Evolutionary/Neural Hybrid Approach to Learning Optimization Problems Dan Ventura Tony R. Martinez Computer Science Department, Brigham Young University, Provo, Utah 84602 e-mail: dan@axon.cs.byu.edu, martinez@cs.byu.edu A |
| ftp://synapse.cs.byu.edu/pub/papers/wilson.jair97.hvdm.ps, 19960926
A revised version of this paper will appear in Journal of Aritificial Intelligence Research (JAIR), 1997. Improved Heterogeneous Distance Functions D. Randall Wilson, Tony R. Martinez e-mail: randy@axon.cs.byu.edu, martinez@cs.byu.edu Computer Science Department, Brigham Young University, Provo, UT |
| ftp://synapse.cs.byu.edu/pub/papers/andersen.wcnn96.dmp_ga.ps, 19960930
Reference: WCNN, pp 177-181, 1996.The Effect of Decision Surface Fitness on Dynamic Multi-layer Perceptron Networks (DMP1) Tim L. Andersen and Tony R. Martinez Computer Science Department, Brigham Young University, Provo, Utah 84602 e-mail: tim@axon.cs.byu.edu, martinez@cs.byu.edu |
| ftp://synapse.cs.byu.edu/pub/papers/andersen.iasted96.dmp2.ps, 19960930
Reference: Proceedings of the IASTED International Conference on Artificial Intelligence, Expert Systems and Neural Networks, pp. 249-252, 1996. Using Multiple Node Types to Improve the Performance of DMP (Dynamic Multilayer Perceptron) Tim L. Andersen and Tony R. Martinez Computer Science Department, |
| ftp://synapse.cs.byu.edu/pub/papers/rudolph.lia96.ps, 19961001
To appear in International Journal of Neural Systems This research is funded in part by grants from Novell Inc. and Word Perfect Corp. LIA: A Location-Independent Transformation for ASOCS Adaptive Algorithm 2 George L. Rudolph and Tony R. Martinez Computer Science Department, Brigham Young University, |
| ftp://synapse.cs.byu.edu/pub/papers/wilson.icml97.prune.ps, 19970206
Submitted to International Conference on Machine Learning (ICML 97), 1997. Instance Pruning Techniques D. Randall Wilson Tony R. Martinez Computer Science Department Brigham Young University Provo, UT 84058 |
| ftp://synapse.cs.byu.edu/pub/papers/wilson.icannga97.rpnn.ps, 19970206
To appear in Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, (ICANNGA 97), April 1997. Improved Center Point Selection for Probabilistic Neural Networks D. Randall Wilson, Tony R. Martinez E-mail: randy@axon.cs.byu.edu, martinez@cs.byu.edu Neural Network |
| ftp://synapse.cs.byu.edu/pub/papers/ventura.icannga97.ps, 19970401
An Artificial Neuron with Quantum Mechanical Properties Dan Ventura and Tony Martinez Neural Networks and Machine Learning Laboratory (http://axon.cs.byu.edu) Department of Computer Science Brigham Young University, Provo, Utah 84602 USA dan@axon.cs.byu.edu, martinez@cs.byu.edu |
| ftp://synapse.cs.byu.edu/pub/papers/zarndt.thesis95.ps, 19970423
A Comprehensive Case Study: An Examination of Machine Learning and Connectionist Algorithms A Thesis Presented to the Department of Computer Science Brigham Young University In Partial Fulfillment of the Requirements for the Degree Master of Science Frederick Zarndt fzarndt@novell.com June 1995 Contents |
| ftp://synapse.cs.byu.edu/pub/papers/wilson.ml97.idibl.ps, 19970607
Advances in Instance-Based Learning D. Randall Wilson and Tony R. Martinez E-mail: randy@axon.cs.byu.edu, martinez@cs.byu.edu Neural Network & Machine Learning Laboratory World-Wide Web: http://axon.cs.byu.edu Computer Science Department Brigham Young University Provo, Utah 84602, USA Tel. (801) |
| ftp://synapse.cs.byu.edu/pub/papers/wilson.phd97.ps, 19970607
Advances in Instance-Based Learning Algorithms A Dissertation Presented to the Department of Computer Science Brigham Young University In Partial Fulfillment of the Requirement for the Degree Doctor of Philosophy by D. Randall Wilson August 1997 ii This dissertation by D. Randall Wilson is accepted in |
| ftp://synapse.cs.byu.edu/pub/papers/wilson.ml97.drop.ps, 19970607
Submitted to Machine Learning, 1997. Reduction Techniques for Exemplar-Based Learning Algorithms D. Randall Wilson and Tony R. Martinez E-mail: randy@axon.cs.byu.edu, martinez@cs.byu.edu Neural Network & Machine Learning Laboratory World-Wide Web: http://axon.cs.byu.edu Computer Science Department |
| ftp://synapse.cs.byu.edu/pub/papers/wilson.ci97.fibl.ps, 19970607
1 DISTANCE-WEIGHTING AND CONFIDENCE IN INSTANCE-BASED LEARNING D. Randall Wilson and Tony R. Martinez E-mail: randy@axon.cs.byu.edu, martinez@cs.byu.edu Neural Network & Machine Learning Laboratory World-Wide Web: http://axon.cs.byu.edu Computer Science Department Brigham Young University Provo, Utah |
| ftp://synapse.cs.byu.edu/pub/papers/t2.ps, 19971021
In Fisher, D., ed., Machine Learning: Proceedings of the Fourteenth International Conference, Morgan Kaufmann Publishers, San Francisco, CA, 1997. Instance Pruning Techniques D. Randall Wilson, Tony R. Martinez Computer Science Department Brigham Young University Provo, UT 84058 randy@axon.cs.byu.edu, |
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