Curriculum Vitae

Tony R. Martinez
Professor of Computer Science, Brigham Young University
Computer Science Department, 3361 TMCB, Brigham Young University, Provo, Utah 84602
Phone: 801-422-6464, Fax: 801-422-0169, E-mail: martinez@cs.byu.edu
Web: http://axon.cs.byu.edu/~martinez 


Education

 

Ph.D. in Computer Science, 1986, UCLA, Dissertation: Adaptive Self-Organizing Logic Networks
M.S. in Computer Science, 1983, UCLA
B.S. in Computer Science, 1982, Brigham Young University

 

Professional Positions


Brigham Young University, Professor of Computer Science, 1998 - Present
Brigham Young University, Department Chair, Computer Science Department, 1999 - 2008

Brigham Young University, Associate Professor of Computer Science, 1993 - 1998
Brigham Young University, Assistant Professor of Computer Science, 1987 - 1993
MCC (Microelectronics and Computer Technology Corp.), MTS, July 86 - Aug. 87
UCLA, Post Graduate Research Engineer, June 85 - June 86
UCLA, Teaching Associate, Oct. 82 - June 85
System Development Corporation, Computer Analyst Senior, July 84 - Nov. 85
NASA Jet Propulsion Laboratories, Computer Analyst, Summer 83

 

Refereed Journal Articles

 

Martinez, T. R., and J. J. Vidal, Adaptive Parallel Logic Networks, Journal of Parallel and Distributed Computing, vol. 5, No. 1, pp. 26-58, Feb. 1988.

 

Martinez, T. R., Smart Memory Architecture and Methods, Future Generation Computer Systems, vol. 6, pp. 145-162, 1990.

 

Martinez, T. R., Adaptive Self-Organizing Concurrent Systems, Progress in Neural Networks, vol. 1, Ch. 5, pp. 105-126, O. Omidvar (Ed), Ablex Publishing, 1990.

 

Martinez, T. R. and Campbell, D. M., A Self-Organizing Binary Decision Tree for Incrementally Defined Rule Based Systems, IEEE Transactions on Systems, Man, and Cybernetics, vol. 21, No. 5, pp. 1231-1238, 1991.

 

Martinez, T. R. and Campbell, D. M., A Self-Adjusting Dynamic Logic Module, Journal of Parallel and Distributed Computing, vol. 11, No. 4, pp. 303-313, 1991.

 

Kemsely, D., Martinez, T. R. and Campbell, D. M.,  A Survey of Neural Network Research and Fielded Applications, International Journal of Neural Networks, vol. 2, No. 2/3/4, pp. 123-133, 1992.

 

Martinez, T. R. and Campbell, D. M., A Survey of Neural Network Research and Fielded Applications, International Journal of Neural Networks, vol. 2, No. 2/3/4, pp. 123-133, 1992.

 

Rudolph, G. and Martinez, T. R., Location Independent Transformations: A General Strategy for Implementing Neural Networks, International Journal on Artificial Intelligence Tools, vol. 3, No. 3, pp. 417-427, 1994.

 

Barker, J. C. and Martinez, T. R., Proof of Correctness for ASOCS AA3 Networks, IEEE Transactions on Systems, Man, and Cybernetics, vol. 24, No. 3, pp. 503-510, 1994.

 

Martinez, T. R., Hughes, B., and Campbell, D. M., Priority ASOCS, Journal of Artificial Neural Networks, vol. 1, No. 3, pp. 403-429, 1994.

 

Van Horn, K. and Martinez, T. R., The Minimum Feature Set Problem, Neural Networks, vol. 7, No. 3, pp. 491-494, 1994.

 

Giraud-Carrier, C. and Martinez, T. R., Analysis of the Convergence and Generalization of AA1, Journal of Parallel and Distributed Computing, vol. 26, pp. 125-131, 1995.

 

Rudolph, G. and Martinez, T. R., An Efficient Transformation for Implementing Two-Layer Feed Forward Neural Networks, Journal of Artificial Neural Networks,, vol. 2, no. 3, pp. 263-282, 1995.

 

Giraud-Carrier, C. and Martinez, T. R., An Integrated Framework for Learning and Reasoning, Journal of Artificial Intelligence Research, vol. 3, pp. 147-185, 1995.

 

Rudolph, G. and Martinez, T. R., A Transformation for Implementing Localist Neural Networks, Neural Parallel and Scientific Computations, vol. 3, no. 2, pp. 173-188, 1995.

 

Rudolph, G. and Martinez, T. R., LIA: A Location-Independent Transformation for ASOCS Adaptive Algorithm 2, International Journal of Neural Systems, vol. 7, no. 5, pp. 639-653, 1996.

 

Wilson, D. R. and Martinez, T. R., Improved Heterogeneous Distance Functions, Journal of Artificial Intelligence Research, vol. 6, no. 1, pp. 1-34, 1997.

 

Rudolph, G. and Martinez, T. R., A Transformation Strategy for Implementing Distributed Multilayer Feedforward Networks: Backpropagation Transformation, Future Generation Computer Systems, vol. 12, no. 6, pp. 547-564, 1997.

 

Ventura, D. and Martinez, T. R., Initializing the Amplitude Distribution of a Quantum StateFoundations of Physics Letters, vol. 12, no. 6, pp. 547-559, 1999.

 

Zeng, X. and Martinez, T. R., A New Relaxation Procedure in the Hopfield Network for Solving Optimization Problems, Neural Processing Letters, vol. 10, pp. 1-12, 1999.

 

Wilson, D. R. and Martinez, T. R., An Integrated Instance-Based Learning Algorithm, Computational Intelligence, vol. 16, no. 1, pp. 1-28, 2000.

 

Zeng, X. and Martinez, T. R., Distribution-Balanced Stratified Cross-Validation for Accuracy EstimationJournal of Experimental and Theoretical Artificial Intelligence, vol. 12, pp. 1-12, 2000.

 

Ventura, D. and Martinez, T. R., Quantum Associative Memory, Information Sciences, vol. 124, no. 1-4, pp. 273-296, 2000.

 

Zeng, X. and Martinez, T. R., Using a Neural Network to Approximate an Ensemble of Classifiers, Neural Processing Letters, vol. 12, pp. 235-237, 2000.

 

Wilson, D. R. and Martinez, T. R.,  Reduction Techniques for Instance-Based Learning Algorithms,  Machine Learning Journal, vol. 38, no. 3, pp. 257-286, 2000.

 

Andersen, T. L. and Martinez, T. R., DMP3: A Dynamic Multi-Layer Perceptron Construction Algorithm, International Journal of Neural Systems, vol. 11, no. 2, pp. 145-166, 2001.

 

Zeng, X. and Martinez, T. R., An Algorithm for Correcting Mislabeled DataIntelligent Data Analysis, vol. 5, no. 6, pp. 491-502, 2001.

 

Istook, E., and Martinez, T. R., Improved Backpropagation Learning in Neural Networks with Windowed Momentum, International Journal of Neural Systems, vol. 12, no. 3&4, pp. 303-318, 2002.

 

Ventura, D. and Martinez, T. R., Quantum Associative Memory, Neurocomputers: Development and Applications (in Russian), N9-10, pp. 34-53, 2002.

 

Wilson, D. R. and Martinez, T. R., The General Inefficiency of Batch Training for Gradient Descent Learning, Neural Networks, vol. 16, no. 10, pp. 1429-1452, 2003.

 

Morring, B. and Martinez, T. R., Weighted Instance Typicality Search (WITS): A Nearest Neighbor Data Reduction AlgorithmIntelligent Data Analysis, vol. 8, no. 1, pp. 61-78, 2004.

 

Rimer, M. and Martinez, T. R., An Adaptive Error Function for Backpropagation Training, Neural Processing Letters, vol. 24, no. 1, pp. 81-92, 2006.

 

Rimer, M. and Martinez, T. R., Classification-Based Objective Functions,  Machine Learning Journal, vol. 63, no. 2, pp. 183-205, 2006.

 

Henderson, E., K., and Martinez, T. R., Constructing Low-Order Discriminant Neural Networks Using Statistical Feature Selection, Journal of Intelligent Systems, vol. 16, no. 1, pp. 27-56, 2007.

 

Giraud-Carrier, C. and Martinez, T. R., Learning by Discrimination: A Constructive Incremental Approach, Journal of Computing (JCP), vol. 2, no. 5, pp. 49-58, 2007.

 

Menke, J. and Martinez, T. R., A Bradley-Terry Artificial Neural Network Model for Individual Ratings in Group Competitions,  Journal of Neural Computing and Applications, vol. 17, no. 2, pp. 175-186, 2008.

 

Zeng, X. and Martinez, T. R., Using Decision Trees and Soft Labeling to Filter Mislabeled DataJournal of Intelligent Systems, vol. 17, no. 4, pp. 331-354, 2008.

 

Menke, J. and Martinez, T. R., Artificial Neural Network Reduction through Oracle Learning, Intelligent Data Analysis, vol. 13, no. 1, pp. 135-149, 2009.

 

Peterson, A. and Martinez, T. R., Reducing Decision Tree Ensemble Size using Parallel Decision DAGs, International Journal on Artificial Intelligence Tools, vol. 18, No. 4, pp. 613-620, 2009.

 

Menke, J. and Martinez, T. R., Improving Supervised Learning by Adapting the Problem to the Learner, International Journal of Neural Systems, vol. 19, no. 1, pp. 1-9, 2009.

 

Peterson, A. and Martinez, T. R., Using Learning Algorithm Behavior to Chart Task Space: The DICES Distance, Intelligent Data Analysis, vol. 14, No. 3, pp. 355-367, 2010.

 

Gashler, M., Ventura, D., and Martinez, T. R., Manifold Learning by Graduated Optimization, IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics - SMCB, vol. 41, no. 6, pp. 1458-1470, 2011.

 

White, S., Martinez, T. R, and Rudolph, G., Automatic Algorithm Development Using New Reinforcement Programming Techniques, Computational Intelligence, vol. 28, no. 2, pp. 176-208, 2012.

 

Gashler, M. and Martinez, T. R., Robust Manifold Learning With CycleCut, vol. 24, no. 1, pp. 57-69, Connection Science, 2012.

 

Monteith, K., Martinez, T. R, Aggregate Certainty Estimators, Computational Intelligence, vol. 29, no. 2, pp. 207-232, 2013.

 

Smith, M., Martinez, T. R., and Giraud-Carrier, C., An Instance Level Analysis of Data Complexity, Machine Learning Journal, vol. 95, no. 2, pp. 225-256, 2014.

 

Gashler, M., Smith, M., Morris, R., and Martinez, T. R., Missing Value Imputation With Unsupervised Backpropagation, Computational Intelligence, DOI: 10.1111/coin.12048, 21 pages, 2014.

 

Smith, M., and Martinez, T. R., A Comparative Evaluation of Curriculum Learning with Filtering and Boosting in Supervised Classification Problems, Computational Intelligence, DOI: 10.1111/coin.12047, 31 pages, 2014.

 

Rudolph, G., and Martinez, T. R., Finding the Real Differences Between Learning Algorithms, International Journal on Artificial Intelligence Tools, vol. 24 DOI: 10.1142/ S0218213015500013, no. 3, 20 pages, 2015.

 

Smith, M., and Martinez, T. R., The Robustness of Majority Voting Compared to Filtering Misclassified Instances in Supervised Classification Tasks, Artificial Intelligence Review, DOI: 10.1007/s10462-016-9518-2, pp. 1-26, 2016.

 

 

Refereed Conference Publications

 

Martinez, T. R., Models of Parallel Adaptive Logic, Proceedings of the 1987 IEEE Systems Man and Cybernetics Conference, pp. 290-296, 1987.

 

Martinez, T. R., Digital Neural Networks, Proceedings of the 1988 IEEE Systems Man and Cybernetics Conference, pp. 681-684, 1988.

 

Martinez, T. R. and M. Lindsey, On the Pseudo Multilayer Learning of Backpropagation, Proceedings of the IEEE Symposium on Parallel and Distributed Processing, pp. 308-315, 1989.

 

Martinez, T. R., Neural Network Applicability: Classifying the Problem Space, Proceedings of the IASTED International Symposium on Expert Systems and Neural Networks, pp. 41-44, 1989.

 

Rudolph, G., and Martinez, T. R., DNA: Towards an Implementation of ASOCS, Proceedings of the IASTED International Symposium on Expert Systems and Neural Networks, pp. 12-15, 1989.

 

Martinez, T. R., Consistency and Generalization of Incrementally Trained Connectionist Models, Proceedings of the International Symposium on Circuits and Systems, pp. 706-709, 1990.

 

Martinez, T. R., Smart Memory: The Memory Processor Model, IFIP International Conference, March, 1990. In Modeling the Innovation: Communications, Automation and Information Systems, Carnevale, Lucertini, and Nicosia (Eds), pp. 481-488, North-Holland, 1990.

 

Mcdonald, K., T. R. Martinez, and D. M. Campbell, A Connectionist Method for Adaptive Real-Time Network Routing, Proceedings of the 4th International Symposium on Artificial Intelligence, pp. 371-377, 1991.

 

Rudolph, G. and Martinez, T. R., An Efficient Static Topology for Modeling ASOCS, International Conference on Artificial Neural Networks. In Artificial Neural Networks, Kohonen, et. al. (Eds), Elsevier Science Publishers, North Holland, pp. 729-734, 1991.

 

Martinez, T. R., ASOCS: Towards Bridging Neural Network and Artificial Intelligence Learning, Proceedings of the 2nd Government Neural Network Workshop, 1991.

 

Wilson, D. R. and T. R. Martinez, The Importance of Multiple Styles of Generalization, Proceedings of the International Conference on Artificial Neural Networks and Expert Systems ANNES'93, pp. 54-57, 1993.

 

Van Horn, K. S. and Martinez, T. R., The BBG1 Rule Induction Algorithm, Proceedings of the AI'93 Australian Joint Conference on Artificial Intelligence, pp. 349-355, 1993.

 

Martinez, T. R., and G. Rudolph, A Learning Model for Adaptive Routing, Proceedings of the International Workshop on Applications of Neural Networks to Telecommunications IWANNT'93, pp. 183-187, 1993.

 

Barker, J. C. and Martinez, T. R., GS: A Network that Learns Important Features, Proceedings of the World Congress on Neural Networks, vol. III, pp. 376-380, 1993.

 

Wilson, D. R. and T. R. Martinez, The Potential of Prototype Styles of Generalization, Proceedings of the AI'93 Australian Joint Conference on Artificial Intelligence, pp. 356-361, 1993.

 

Martinez, T. R., J.C. Barker, and C. Giraud-Carrier, A Generalizing Adaptive Discriminant Network, Proceedings of the World Congress on Neural Networks, vol. I, pp. 613-616, 1993.

 

Giraud-Carrier, C. and T. R. Martinez, Using Precepts to Augment Training Set Learning, Proceedings of the International Conference on Artificial Neural Networks and Expert Systems ANNES'93, pp. 46-51, 1993.

 

Barker, J. C. and Martinez, T. R., Generalization by Controlled Intersection of Examples, Proceedings of the AI'93 Australian Joint Conference on Artificial Intelligence, pp. 323-327, 1993.

 

Martinez, T. R., and B. Hughes, Towards a General Distributed Platform for Learning and Generalization, Proceedings of the Conference on Artificial Neural Networks and Expert Systems ANNES'93, pp. 216-219, 1993.

 

Bertelsen, R. and T. R. Martinez, Extending ID3 through Discretization of Continuous Inputs, Proceedings of FLAIRS'94 Florida Artificial Intelligence Research Symposium, pp. 122-125, 1994.

 

Stout, M., Salmon, L., Rudolph, G., and Martinez, T. R., A Multi-Chip Module Implementation of a Neural Network, Proceedings of the IEEE Multi-Chip Module Conference MCMC-94, pp. 20-25, 1994.

 

Barker, J. C. and Martinez, T. R., Generalization by Controlled Expansion of Examples, Proceedings of the 7th International Symposium on Artificial Intelligence, pp. 142-149, 1994.

 

Giraud-Carrier, C. and T. R. Martinez, Seven Desirable Properties of Artificial Learning Systems, Proceedings of FLAIRS'94 Florida Artificial Intelligence Research Symposium, pp. 16-20, 1994.

 

Stout, M., Rudolph, G., Martinez, T. R., and Salmon, L., A VLSI Implementation of a Parallel Self-Organizing Learning Model, Proceedings of the 12th International Conference on Pattern Recognition, vol. 3, pp. 373-376, 1994.

 

Giraud-Carrier, C. and T. R. Martinez, An Incremental Learning Model for Commonsense Reasoning, Proceedings of the 7th International Symposium on Artificial Intelligence, pp. 134-141, 1994.

 

Ventura, D. and T. R. Martinez, BRACE: A Paradigm for the Discretization of Analog Data, Proceedings of FLAIRS'94 Florida Artificial Intelligence Research Symposium, pp. 117-121, 1994.

 

Rudolph, G. and Martinez, T. R., A Transformation for Implementing Neural Networks with Localist Properties, Intelligent Systems, E. A. Yfantis (ed.), Vol. 1, pp. 637-645, Kluwer Academic Publishers, 1995.

 

Andersen, T. and Martinez, T. R., Learning and Generalization with Bounded Order Rule Sets, Proceedings of the 10th International Symposium on Computer and Information Sciences, pp. 419-426, 1995.

 

Giraud-Carrier, C. and T. R. Martinez, AA1*: A Dynamic Incremental Network that Learns by Discrimination, Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 45-48, 1995.

 

Barker, J. C. and Martinez, T. R., Efficient Construction of Networks for Learned Representations with General to Specific Relationships, Intelligent Systems, E. A. Yfantis (ed.), Vol. 1, pp. 617-625, Kluwer Academic Publishers, 1995.

 

Ventura, D. and Martinez, T. R., An Empirical Comparison of Discretization Models, Proceedings of the 10th International Symposium on Computer and Information Sciences, pp. 443-450, 1995.

 

Andersen, T. and Martinez, T. R., A Provably Convergent Dynamic Training Method for Multilayer Perceptron Networks, Proceedings of the 2nd International Symposium on Neuroinformatics and Neurocomputers, pp. 77-84, 1995.

 

Rudolph, G. and Martinez, T. R., A Transformation for Implementing Efficient Dynamic Backpropagation Neural Networks, Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 41-44, 1995.

 

Giraud-Carrier, C. and Martinez, T. R., An Efficient Metric for Heterogeneous Inductive Learning Applications in the Attribute-Value Language, Intelligent Systems, E. A. Yfantis (ed.), Vol. 1, pp. 341-350, Kluwer Academic Publishers, 1995.

 

Ventura, D., T. Andersen, and T. R. Martinez, Using Evolutionary Computation to Generate Training Set Data for Neural Networks, Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 468-471, 1995.

 

Van Horn, K. S. and Martinez, T. R., Extending Occam's Razor, Intelligent Systems, E. A. Yfantis (ed.), Vol. 1, pp. 249-259, Kluwer Academic Publishers, 1995.

 

Ventura, D. and Martinez, T. R., Using Multiple Statistical Prototypes to Classify Continuously Valued Data, Proceedings of the 2nd International Symposium on Neuroinformatics and Neurocomputers, pp. 238-245, 1995.

 

Andersen, T. and Martinez, T. R., NP-Completeness of Minimum Rule Sets, Proceedings of the 10th International Symposium on Computer and Information Sciences, pp. 411-418, 1995.

 

Wilson, D. R. and T. R. Martinez, Instance-Based Learning with Genetically Derived Attribute Weights, Proceedings of AIE'96 International Conference on Artificial Intelligence, Expert Systems and Neural Networks, pp. 11-14, 1996.

 

Ventura, D. and T. R. Martinez, Robust Optimization Using Training Set Evolution, Proceedings of the 1996 IEEE International Conference on Neural Networks ICNN, pp. 524-528, 1996.

 

Wilson, D. R. and T. R. Martinez, Value Difference Metrics for Continuously Valued Attributes, Proceedings of AIE'96 International Conference on Artificial Intelligence, Expert Systems and Neural Networks, pp. 74-78, 1996.

 

Ventura, D. and T. R. Martinez, A General Evolutionary/Neural Hybrid Approach to Learning Optimization Problems, Proceedings of WCNN'96 World Congress on Neural Networks, pp. 1091-1095, 1996.

 

Anderson, T. L. and T. R. Martinez, Using Multiple Node Types to Improve the Performance of Dynamic Multilayer Perceptrons, Proceedings of AIE'96 International Conference on Artificial Intelligence, Expert Systems and Neural Networks, pp. 249-252, 1996.

 

Wilson, D. R. and T. R. Martinez, Heterogeneous Radial Basis Function Networks, Proceedings of the 1996 IEEE International Conference on Neural Networks ICNN, pp. 1263-1267, 1996.

 

Ventura, D. and T. R. Martinez, Concerning a General Framework for the Development of Intelligent Systems, Proceedings of AIE'96 International Conference on Artificial Intelligence, Expert Systems and Neural Networks, pp. 44-47, 1996.

 

Anderson, T. L. and T. R. Martinez, The Effect of Decision Surface Fitness on Dynamic Multilayer Perceptron Networks (DMP1), Proceedings of WCNN'96 World Congress on Neural Networks, pp. 177-181, 1996.

 

Wilson, D. R. and Martinez, T. R., Real Valued Schemata Search Using Statistical Confidence, Proceedings of the International Workshop on Neural Networks and Neurocontrol, pp. 256-266, 1997.

 

Wilson, D. R. and T. R. Martinez, Improved Center Point Selection for Radial Basis Function Networks, Proceedings of the 1997 International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 514-517, 1997.

 

Andersen, T. L. and Martinez, T. R., Genetic Algorithms and Higher Order Perceptron Networks, Proceedings of the International Workshop on Neural Networks and Neurocontrol, pp. 217-223, 1997.

 

Wilson, D. R. and T. R. Martinez, Bias and the Probability of Generalization, Proceedings of the International Conference on Intelligent Information Systems, pp. 108-114, 1997.

 

Ventura, D. and T. R. Martinez, An Artificial Neuron with Quantum Mechanical Properties, Proceedings of the 1997 International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 482-485, 1997.

 

Wilson, D. R. and T. R. Martinez, Instance Pruning Techniques, Proceedings of the Fourteenth International Conference on Machine Learning (ICML'97), pp. 403-411, 1997.

 

Ventura, D. and Martinez, T. R., Using Evolutionary Computation to Facilitate Development of Neurocontrol,  Proceedings of the International Workshop on Neural Networks and Neurocontrol, 1997.

 

Ventura, D. and Martinez, T. R., Quantum Associative Memory with Exponential Capacity, Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN'98, pp. 509-513, 1998.

 

Andersen, T. L. and Martinez, T. R., Constructing Higher Order Perceptrons with Genetic Algorithms, Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN'98, pp. 1920-1925, 1998.

 

Ventura, D. and Martinez, T. R., Optimal Control Using a Neural/Evolutionary Hybrid System, Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN'98, pp. 1036-1040, 1998.

 

Andersen, T. L. and Martinez, T. R., The Little Neuron that Could, Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN'99, CD Paper #191, 1999.

 

Zeng, X. and Martinez, T. R., Improving the Performance of the Hopfield Network by using a Relaxation Rate, International Conference on Artificial Neural Networks and Genetic Algorithms ICANNGA’99, pp. 73-77, 1999.

 

Wilson, D. R., Ventura D., Moncur B.,  and Martinez, T. R., The Robustness of Relaxation Rates in Constraint Satisfaction Networks, Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN'99,  CD Paper #162, 1999.

 

Ventura, D. and Martinez, T. R., A Quantum Associative Memory Based on Grover’s AlgorithmInternational Conference on Artificial Neural Networks and Genetic Algorithms ICANNGA’99, pp. 22-27, 1999.

 

Zeng, X. and Martinez, T. R., Extending the Power and Capacity of Constraint Satisfaction NetworksProceedings of the IEEE International Joint Conference on Neural Networks IJCNN'99,  CD Paper #190, 1999.

 

Andersen, T. L. and Martinez, T. R., Cross-Validation and MLP Architecture SelectionProceedings of the IEEE International Joint Conference on Neural Networks IJCNN'99,  CD Paper #192, 1999.

 

Zeng, X. and Martinez, T. R., A New Activation Function in the Hopfield Network for Solving Optimization Problems, International Conference on Artificial Neural Networks and Genetic Algorithms ICANNGA’99, pp. 67-72, 1999.

 

Wilson, D. R. and Martinez, T. R, Combining Cross-Validation and Confidence to Measure Fitness, Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN'99,  CD Paper #163, 1999.

 

Ventura D., Wilson, D. R., Moncur B., and Martinez, T. R., A Neural Model of Centered Tri-gram Speech Recognition, Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN'99,  CD Paper #2188, 1999.

 

Zeng, X. and Martinez, T. R., Rescaling the Energy Function in Hopfield Networks, Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’00, Vol. 6, pp. 498-504, 2000.

 

Jensen, L. S. and Martinez, T. R., Improving Text Classification Using Conceptual and Contextual Features, Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD’00, pp. 101-102, 2000.

 

Wilson, D. R. and Martinez, T. R., The Inefficiency of Batch Training on Large Training Sets, Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’00, Vol. 2, pp. 113-117, 2000.

 

Zeng, X. and Martinez, T. R., Improving the Hopfield Network through Beam Search, Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’01, pp. 1162-1167, 2001.

 

Rimer, M., Andersen, T., and Martinez, T. R., Lazy Training: Improving Backpropagation Learning through Network Interaction, Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’01, pp. 2007-2112, 2001.

 

Clift, F. and Martinez, T. R., Improved Hopfield Nets by Training with Noisy Data, Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’01, pp. 1138-1143, 2001.

 

Zeng, X. and Martinez, T. R., Graded Rescaling in Hopfield Networks, International Conference on Artificial Neural Networks and Genetic Algorithms ICANNGA’01, pp. 63-66, 2001.

 

Andersen, T. L. and Martinez, T. R., Optimal Artificial Neural Network Architecture Selection for Bagging, Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’01, pp. 790-795, 2001.

 

Wilson, D. R and Martinez, T. R., The Need for Small Learning Rates on Large Problems, Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’01, pp. 115-119, 2001.

 

Rimer, M., Andersen, T., and Martinez, T. R., Speed Training: Improving Learning Speed for Large Data Sets, Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’01, pp. 2662-2666, 2001.

 

Henderson, E., and Martinez, T. R., Pair Attribute Learning: Network Construction Using Pair Features, Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’02, pp. 2556-2561, 2002.

 

Menke, J., Peterson, A., Rimer, M, and Martinez, T. R., Neural Network Simplification through Oracle Learning, Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’02, pp. 2482-2497, 2002.

 

Rimer, M., Martinez, T. R., and D. R. Wilson, Improving Speech Recognition Learning through Lazy Training, Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’02, pp. 2568-2573, 2002.

 

Zeng, X., and Martinez, T. R., Optimization by Varied Beam Search in Hopfield Networks, Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’02, pp. 913-918, 2002.

 

Menke, J., and Martinez, T. R., Simplifying OCR Neural Networks with Oracle Learning, Proceedings of the IEEE International Workshop on Soft-Computing Techniques in Instrumentation, Measurement, and Related Applications, pp. 6-13, 2003.

 

Zeng, X., and Martinez, T. R., A Noise Filtering Method Using Neural Networks, Proceedings of the IEEE International Workshop on Soft-Computing Techniques in Instrumentation, Measurement, and Related Applications, pp. 26-31, 2003.

 

Rimer, M., and Martinez, T. R.Softprop: Softmax Neural Network Backpropagation Learning, Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’04, pp. 979-984, 2004.

 

Menke, J., and Martinez, T. R.Using Permutations Instead of Student's t Distribution for p-values in Paired-Difference Algorithm Comparisons, Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’04, pp. 1331-1336, 2004.

 

Zeng, X., and Martinez, T. R.Feature Weighting Using Neural Networks, Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’04, pp. 1327-1330, 2004.

 

Menke, Joshua and Martinez, Tony R. Domain expert approximation through oracle learning. Proceedings of the 13th European Symposium on Artificial Neural Networks (ESANN 2005), pp. 205-210, 2005.

 

Peterson, A., and Martinez, T. R., Estimating the Potential for Combining Learning Models, Proceedings of the ICML Workshop on Meta-Learning, pp. 68-75, 2005.

 

Giraud-Carrier, C., and Martinez, T. R., A Constructive Incremental Learning Algorithm for Binary Classification Tasks,  Proceedings of SMCals/06, pp. 213-218, 2006.

 

Gashler, M., Ventura, D., and Martinez, T. R., Iterative Nonlinear Dimensionality Reduction with Manifold Sculpting,  Proceedings of NIPS’ 07 (Advances in Neural Information Processing Systems), pp. 513-520, 2007.

 

Gashler, M., Giraud-Carrier, C., and Martinez, T. R., Decision Tree Ensemble: Small Heterogeneous is Better than Large Homogeneous, Proceedings of ICMLA’08 (International Conference on Machine Learning Applications), pp. 900-905, 2008.

 

Monteith, K., Martinez, T. R, and Ventura, D.,  Computational Modeling of Emotional Content in Music, Proceedings of CogSci-2010 (International Conference on Cognitive Science), pp. 2356-2361, 2010.

 

White, S., Martinez, T. R, and Rudolph, G., Generating Three Binary Addition Algorithms using Reinforcement Programming, Proceedings of ACMSE-2010, 2010.

 

Perry, K., Martinez, T. R, and Ventura, D., Automatic Generation of Music for Inducing Emotive Response, Proceedings of ICCC-X (International Conference on Computational Creativity), pp. 140-149, 2010.

 

Monteith, K., Martinez, T. R, Using Multiple Measures to Predict Confidence in Instance Classification, Proceedings of IJCNN-2010 (International Joint Conference on Neural Networks), pp. 4192-4199, 2010.

 

Smith, M., Clement, M., Martinez, T. R, and Snell, Q., Time Series Gene Expression Prediction using Neural Networks with Hidden Layers, Proceedings of the 7th Annual Biotechnology and Bioinformatics Symposium (BIOT 2010), pp. 67-69, 2010.

 

White, S., Martinez, T. R, and Rudolph, G., Generating a Novel Sort Algorithm using Reinforcement Programming, Proceedings of CEC-2010 (International Conference on Evolutionary Computation), pp. 2633-2640, 2010.

 

Gashler, M., and Martinez, T. R., Temporal Nonlinear Dimensionality Reduction, Proceedings of IJCNN-2011 (International Joint Conference on Neural Networks), pp. 1959-1966, 2011.

 

Smith, M., and Martinez, T. R., Improving Classification Accuracy by Identifying and Removing Instances that Should be Misclassified, Proceedings of IJCNN-2011 (International Joint Conference on Neural Networks), pp. 2690-2697, 2011.

 

Monteith, K., Martinez, T. R, and Ventura, D., Automatic Generation of Emotionally Targeted Soundtracks, Proceedings of ICCC-11 (International Conference on Computational Creativity), pp. 60-62, 2011.

 

Peterson, A., Martinez, T., and Rudolph, G., On the Structure of Algorithm Spaces, Proceedings of IJCNN-2011 (International Joint Conference on Neural Networks), pp. 658-665, 2011.

 

Montieth, K., Carroll, J., Seppi, K., and Martinez, T. R., Turning Bayesian Model Averaging into Bayesian Model Combination, Proceedings of IJCNN-2011 (International Joint Conference on Neural Networks), pp. 2657-2663, 2011.

 

Gashler, M., and Martinez, T. R., Tangent Space Guided Intelligent Neighbor Finding, Proceedings of IJCNN-2011 (International Joint Conference on Neural Networks), pp. 2617-2624, 2011.

 

Montieth, K., Martinez, T. R, and Ventura, D., Automatic Generation of Melodic Accompaniments for Lyrics, Proceedings of ICCC-12 (International Conference on Computational Creativity), pp. 97-94, 2012.

 

Montieth, K., Brown, B., Ventura, D. and Martinez, T. R, Automatic Generation of Music for Inducing Physiological Response, Proceedings of CogSci 2013, pp. 3098-3103, 2013.

 

Smith, M., Mitchell, L., Giraud-Carrier, C., and Martinez, T. R., Recommending Learning Algorithms and Their Associated Hyperparameters, Proceedings of MetaSel-2014 (ECAI 2014 Workshop on Meta-learning and Algorithm Selection), pp. 39-40, 2014.

 

Smith, M., White, A., Giraud-Carrier, C., and Martinez, T. R., An Easy to Use Repository for Comparing and Improving Machine Learning Algorithm Usage, Proceedings of MetaSel-2014 (ECAI 2014 Workshop on Meta-learning and Algorithm Selection), pp. 41-48, 2014.

 

Smith, Michael R. and Martinez, Tony. Reducing the Effects of Detrimental Instances. In Proceedings of the 13th International Conference on Machine Learning and Applications, pp. 183-188, 2014.

 

Tensmeyer, Christopher, and Martinez, Tony, CONFIRM - Clustering Of Noisy Form Images using Robust Metrics, Family History Technology Workshop 2015 (FHTW2015) (http://fht.byu.edu/), 2015.

 

Smith, M., Gashler, M. and Martinez, T. R., A Hybrid Latent Variable Neural Network Model for Recommendation, Proceedings of IJCNN-2015 International Joint Conference on Neural Networks, 2015.

 

Smith, M., and Martinez, T. R., Using Classifier Diversity to Handle Label Noise, Proceedings of IJCNN-2015 International Joint Conference on Neural Networks, 2015.

 

Smith, M., Martinez, T. R, and Giraud-Carrier, C., The Potential Benefits of Data Set Filtering and Learning Algorithm Hyperparameter Optimization, Proceedings of MetaSel-2015 (ECAI 2015 Workshop on Meta-learning and Algorithm Selection), pp. 3-15, 2015.

 

Tensmeyer, Christopher, and Martinez, Tony, Document Image Binarization with Fully Convolutional Neural Networks, In Proceedings of the ICDAR 2017 International Conference on Document Analysis and Recognition, 2017.

 

Tensmeyer, Christopher, and Martinez, Tony, Analysis of Convolutional Neural Networks for Document Image Classification, In Proceedings of the ICDAR 2017 International Conference on Document Analysis and Recognition, 2017.

 

Tensmeyer, Christopher, and Martinez, Tony, Convolutional Neural Networks for Font Classification, In Proceedings of the ICDAR 2017 International Conference on Document Analysis and Recognition, 2017.

 

Other Refereed Publications and Patents

 

Martinez, T. R., Adaptive Self-Organizing Logic Networks, Ph.D. Dissertation, UCLA Technical Report - CSD 860093, 1986.

 

Martinez, T. R., ASOCS: A Multilayered Connectionist Network with Guaranteed Learning of Arbitrary Mappings, Presented at 2nd IEEE International Conference on Neural Networks, 1988.

 

Martinez, T. R., On the Expedient Use of Neural Networks, Neural Networks, vol. 1, S1, p. 552, Presented at the 1st Meeting of the International Neural Network Society, 1988.

 

Barker, J. C. and Martinez, T. R., Learning and Generalization Controlled by Contradiction, Proceedings of the International Conference on Artificial Neural Networks, 1993.

 

Andersen, T. and Martinez, T. R., Learning and Generalization with Bounded Order Critical Feature Sets, Proceedings of the AI'93 Australian Joint Conference on Artificial Intelligence, pp. 450, 1993.

 

Martinez, T. R., Discussion on Neurocomputers after Ten Years, Neural Network World, vol.1-2, editors A. A. Frolov and A. A. Ezhov, pp. 103-171, 1999.

 

Martinez, T. R., Moncur, B., Shepherd, L., Parr, R., Wilson, D. R., and Hansen, C., Method and Apparatus for Signal Classification Using a Multilayer Network, United States Patent No. 6,208,963, 2001.

 

Martinez, T. R., Zeng, X., Multiple Output Relaxation Machine Learning Model, United States Patent No. 8,352,389, 2013.

 

Martinez, T. R., Zeng, X., and Morris, R., Hierarchical Based Sequencing Machine Learning Model, United States Patent No. 8,812,417, 2014.

 

Martinez, T. R., Zeng, X., Instance Weighted Learning Machine Learning Model, United States Patent No. 8,706,658, 2014.

 

Honors and Awards

 

 

Professional Activities