Publications - NNML Laboratory

This page contains the titles and references of papers written by members of the BYU Neural Networks and Machine Learning (NNML) Research Group. Clicking on the title will allow you to see the abstracts of the paper in the more detailed list. Postscript files are available for each paper.

Andersen, Tim L. and Martinez, Tony R. DMP3: a dynamic multi-layer perceptron construction algorithm. International Journal of Neural Systems, volume 2, pages 145–166, 2001.

Andersen, Tim L. and Rimer, Michael E. and Martinez, Tony R. Optimal artificial neural network architecture selection for voting. In Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’01, pages 790–795, 2001.

Andersen, Tim L. and Martinez, Tony R. Optimal artificial neural network architecture selection for bagging. In Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’01, pages 790–795, 2001.

Andersen, Tim L. and Martinez, Tony R. The little neuron that could. In Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’99, CD paper #191, 1999.

Andersen, Tim L. and Martinez, Tony R. Cross validation and MLP architecture selection. In Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’99, CD paper #192, 1999.

Andersen, Tim L. and Martinez, Tony R. Constructing higher order perceptrons with genetic algorithms. In Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’98, pages 1920–1925, 1998.

Andersen, Tim L. and Martinez, Tony R. Wagging: a learning approach which allows single layer perceptrons to outperform more complex learning algorithms. In Submitted to IEEE Transactions on Neural Networks, 1997.

Andersen, Tim L. and Martinez, Tony R. Genetic algorithms and higher order perceptron networks. In Proceedings of the International Workshop on Neural Networks and Neurocontrol, pages 217–223, 1997.

Andersen, Tim L. and Martinez, Tony R. Using multiple node types to improve the performance of DMP (dynamic multilayer perceptron). In Proceedings of the IASTED International Conference on Artificial Intelligence, Expert Systems and Neural Networks, pages 249–252, 1996.

Andersen, Tim L. and Martinez, Tony R. The effect of decision surface fitness on dynamic multi-layer perceptron networks. In Proceedings of the World Congress on Neural Networks , pages 177–181, 1996.

Andersen, Tim L. Learning and generalization with bounded order rule sets. Master’s thesis, Brigham Young University, April 1995.

Andersen, Tim L. and Martinez, Tony R. Learning and generalization with bounded order rule sets. In Proceedings of the 10th International Symposium on Computer and Information Sciences, pages 419–426, 1995.

Andersen, Tim L. and Martinez, Tony R. NP-completeness of minimum rule sets. In Proceedings of the 10th International Symposium on Computer and Information Sciences, pages 411–418, 1995.

Andersen, Tim L. and Martinez, Tony R. A provably convergent dynamic training method for multilayer perceptron networks. In Proceedings of the 2nd International Symposium on Neuroinformatics and Neurocomputers, pages 77–84, 1995.

Andersen, Tim L. and Martinez, Tony R. Learning and generalization with bounded order critical feature sets. In Proceedings of the AI’93 Australian Joint Conference on Artificial Intelligence, page 450, 1993.

Barker, J. Cory and Martinez, Tony R. Efficient construction of networks for learned representations with general to specific relationships. Yfantis, Evangelos A., editor, Intelligent Systems, volume 1, pages 617–625, Kluwer Academic Publishers, 1995.

Barker, J. Cory. Eclectic Machine Learning. PhD thesis, Brigham Young University, February 1994.

Barker, J. Cory and Martinez, Tony R. Proof of correctness for ASOCS AA3 networks. IEEE Transactions on Systems, Man, and Cybernetics, volume 3, pages 503–510, 1994.

Barker, J. Cory and Martinez, Tony R. Generalization by controlled expansion of examples. In Proceedings of The Seventh International Symposium on Artificial Intelligence, pages 142–149, 1994.

Barker, J. Cory and Martinez, Tony R. GS: a network that learns important features. In Proceedings of The World Congress on Neural Networks, volume 3, pages 376–380, July 1993.

Barker, J. Cory and Martinez, Tony R. Generalization by controlled intersection of examples. In Proceedings of The Sixth Australian Joint Conference on Artificial Intelligence, pages 323–327, 1993.

Barker, J. Cory and Martinez, Tony R. Learning and generalization controlled by contradiction. In Proceedings of The International Conference on Artificial Neural Networks, 1993.

Bertelsen, Rick and Martinez, Tony R. Extending ID3 through discretization of continuous inputs. In Proceedings of FLAIRS’94 Florida Artificial Intelligence Research Symposium, pages 122–125, 1994.

Bertelsen, Rick. Automatic feature extraction in machine learning. Master’s thesis, Brigham Young University, 1994.

Chan, Heather and Ventura, Dan. Automatic composition of themed mood pieces. In Proceedings of the International Joint Workshop on Computational Creativity, pages 109–115, September 2008.

Clift, Fred and Martinez, Tony R. Improved hopfield nets by training with noisy data. In Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’01, pages 1138–1143, 2001.

Dickerson, Kyle and Ventura, Dan. Using self-organizing maps to implicitly model preference for a musical query-by-content system. In Proceedings of the International Joint Conference on Neural Networks, pages 705–710, June 2009.

Drake, Adam and Ventura, Dan. Search techniques for Fourier-based learning. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 1040–1045, July 2009. (First appeared in Proceedings of the AAAI Workshop on Search in Artificial Intelligence and Robotics, 2008).

Drake, Adam and Ringger, Eric and Ventura, Dan. Sentiment regression: using real-valued scores to summarize overall document sentiment. In Proceedings of the IEEE International Conference on Semantic Computing, pages 152–157, August 2008.

Drake, Adam and Ventura, Dan. Comparing high-order boolean features. In Proceedings of the Joint Conference on Information Sciences, pages 428–431, July 2005.

Drake, Adam and Ventura, Dan. A practical generalization of fourier-based learning. In ICML ’05: Proceedings of the 22nd International Conference on Machine Learning, pages 185–192, New York, NY, USA, 2005. ACM Press.

Fulda, Nancy and Ventura, Dan. Predicting and preventing coordination problems in cooperative learning systems. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 780–785, Hyderabad, India, January 2007.

Fulda, Nancy and Ventura, Dan. Learning a rendezvous task with dynamic joint action perception. In Proceedings of the International Joint Conference on Neural Networks, pages 627–632, Vancouver, BC, July 2006.

Fulda, Nancy and Ventura, Dan. Incremental policy learning: an equilibrium selection algorithm for reinforcement learning agents with common interests. In Proceedings of the International Joint Conference on Neural Networks, pages 1121–1126, July 2004.

Fulda, Nancy and Ventura, Dan. Target sets: a tool for understanding and predicting the behavior of interacting q-learners. In Proceedings of the Joint Conference on Information Sciences, pages 1549–1552, September 2003.

Fulda, Nancy and Ventura, Dan. Concurrently learning neural nets: encouraging optimal behavior in reinforcement learning systems.. In IEEE International Workshop on Soft Computing Techniques in Instrumentation, Measurement, and Related Applications (SCIMA), May 2003.

Fulda, Nancy and Ventura, Dan. Dynamic joint action perception for q-learning agents.. In To Appear in Proceedings of the 2003 International Conference on Machine Learning and Applications, Los Angeles, CA, 2003.

Peterson, Todd S. and Owens, Nancy and Carroll, James L. Towards automatic shaping in robot navigation.. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2001.

Carroll, James L. and Peterson, Todd S. and Owens, Nancy. Memory-guided exploration in reinforcement learning.. In Proceedings of the INNS-IEEE International Joint Conference on Neural Networks (IJCNN), 2001.

Owens, Nancy and Peterson, Todd S. Using a reinforcement learning controller to overcome simulator/environment discrepancies.. In Proceedings of the IEEE Conference on Systems, Man, and Cybernetics, 2001.

Gashler, Michael S. and Giraud-Carrier, Christophe and Martinez, Tony. Decision tree ensemble: small heterogeneous is better than large homogeneous. In Seventh International Conference on Machine Learning and Applications, 2008. ICMLA ’08., pages 900–905, Dec. 2008.

Gashler, Michael S. and Ventura, Dan and Martinez, Tony. Iterative non-linear dimensionality reduction with manifold sculpting. In Platt, J.C. and Koller, D. and Singer, Y. and Roweis, S., editor, Advances in Neural Information Processing Systems 20, pages 513–520, MIT Press, Cambridge, MA, 2008.

Giraud-Carrier, Christophe and Martinez, Tony R. Learning by discrimination: a constructive incremental approach. Journal of Computers, volume 2 (7), pages 49–58, September 2007.

Giraud-Carrier, Christophe and Martinez, Tony R. A constructive incremental learning algorithm for binary classification tasks. In Proceedings of SMCals/06, pages 213–218, 2006.

Giraud-Carrier, Christophe and Martinez, Tony R. An efficient metric for heterogeneous inductive learning applications in the attribute-value language. Yfantis, Evangelos A., editor, Intelligent Systems), volume 1, pages 341–350, Kluwer Academic Publishers, 1995.

Giraud-Carrier, Christophe and Martinez, Tony R. An integrated framework for learning and reasoning. Journal of Artificial Intelligence Research, volume 3, pages 147–185, 1995.

Giraud-Carrier, Christophe and Martinez, Tony R. AA1*: a dynamic incremental network that learns by discrimination. In Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA’95), pages 45–48, 1995.

Giraud-Carrier, Christophe and Martinez, Tony R. Analysis of the convergence and generalization of AA1. Journal of Parallel and Distributed Computing, volume 26, pages 125–131, 1995.

Giraud-Carrier, Christophe. On Integrating Inductive Learning with Prior Knowledge and Reasoning. PhD thesis, Brigham Young University, December 1994.

Giraud-Carrier, Christophe and Martinez, Tony R. Seven desirable properties for artificial learning systems. In Proceedings of FLAIRS’94 Florida Artificial Intelligence Research Symposium, pages 16–20, 1994.

Giraud-Carrier, Christophe and Martinez, Tony R. An incremental learning model for commonsense reasoning. In Proceedings of the Seventh International Symposium on Artificial Intelligence (ISAI’94), pages 134–141, 1994.

Giraud-Carrier, Christophe and Martinez, Tony R. Using precepts to augment training set learning. In Proceedings of the First New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems (ANNES’93), pages 46–51, November 1993.

Giraud-Carrier, Christophe. A precept-driven learning algorithm. Master’s thesis, Brigham Young University, April 1993.

Goodman, Eric and Ventura, Dan. Spatiotemporal pattern recognition in liquid state machines. In Proceedings of the International Joint Conference on Neural Networks, pages 7979–7584, Vancouver, BC, July 2006.

Goodman, Eric and Ventura, Dan. Effectively using recurrently connected spiking neural networks. In Proceedings of the International Joint Conference on Neural Networks, pages 1542–1547, July 2005.

Goodman, Eric and Ventura, Dan. Time invariance and liquid state machines. In Proceedings of the Joint Conference on Information Sciences, pages 420–423, July 2005.

Hart, Edward F. Extending ASOCS to training-set-style data. Master’s thesis, Brigham Young University, August 1992.

Henderson, Eric and Martinez, Tony R. Constructing low-order discriminant neural networks using statistical feature selection. Journal of Intelligent Systems, volume 14, 2005.

Henderson, Eric and Martinez, Tony R. Pair attribute learning: network construction using pair features. In Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’02, pages 2556–2561, 2002.

Hughes, Brent W. Prioritized rule systems. Master’s thesis, Brigham Young University, November 1989.

Istook, Butch and Martinez, Tony R. Improved backpropagation learning in neural networks with windowed momentum. International Journal of Neural Systems, volume 3&4, pages 303–318, 2002.

Jensen, Lee S. and Martinez, Tony R. Improving text classification using conceptual and contextual features. pages 101–102, KDD 2000, Text Mining Workshop, Boston. 2000.

Lundell, Jared and Ventura, Dan. A data-dependent distance measure for transductive instance-based learning. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pages 2825–2830, October 2007.

Martinez, Tony R. and Hughes, Brent W. and Campbell, Douglas M. Priority ASOCS. Journal of Artificial Neural Networks , volume 3, pages 403–429, 1994.

Martinez, Tony R. and Barker, J. Cory and Giraud-Carrier, Christophe. A generalizing adaptive discriminant network. In Proceedings of the World Congress on Neural Networks, volume 1, pages 613–616, 1993.

Martinez, Tony R. and Hughes, Brent W. Towards a general distributed platform for learning and generalization. In Proceedings of the Conference on Artificial Neural Networks and Expert Systems ANNES’93, pages 216–219, 1993.

Martinez, Tony R. and Rudolph, George L. A learning model for adaptive network routing. In Proceedings of the International Workshop on Applications of Neural Networks to Telecommunications IWANNT’93, pages 183–187, 1993.

Kemsley, David and Martinez, Tony R. and Campbell, Douglas M. A survey of neural network research and fielded applications. International Journal of Neural Networks, volume 2/3/4, pages 123–133, 1992.

Martinez, Tony R. and Campbell, Douglas M. A self-adjusting dynamic logic module. Journal of Parallel and Distributed Computing, volume 4, pages 303–313, 1991.

Martinez, Tony R. and Campbell, Douglas M. A self-organizing binary decision tree for incrementally defined rule based systems. In IEEE Transactions on Systems, Man, and Cybernetics, volume 5, pages 1231–1238, 1991.

Martinez, Tony R. ASOCS: towards bridging neural network and artificial intelligence learning. In Proceedings of the 2nd Government Neural Network Workshop, 1991.

McDonald, Kelly C. and Martinez, Tony R. and Campbell, Douglas M. A connectionist method for adaptive real-time network routing. In Proceedings of the 4th International Symposium on Artificial Intelligence, pages 371–377, 1991.

Martinez, Tony R. Smart memory: the memory processor model. In IFIP International Conference, 1990. In Modeling the Innovation: Communications, Automation and Information Systems, Carnevale, Lucertini, and Nicosia (Eds), North-Holland, pages 481–488, 1990.

Martinez, Tony R. Consistency and generalization of incrementally trained connectionist models. In Proceedings of the International Symposium on Circuits and Systems, pages 706–709, 1990.

Martinez, Tony R. Progress in Neural Networks, ch. 5. Omidvar, Omid, editor, volume 1, chapter Adaptive Self-Organizing Concurrent Systems, pages 105–126, 1990. Ablex Publishing.

Martinez, Tony R. Smart memory architecture and methods. Future Generation Computer Systems, volume 6, pages 145–162, 1990.

Martinez, Tony R. and Lindsey, M. On the pseudo multilayer learning of backpropagation. In Proceedings of the IEEE Symposium on Parallel and Distributed Processing, pages 308–315, 1989.

Martinez, Tony R. Neural network applicability: classifying the problem space. In Proceedings of the IASTED International Symposium on Expert Systems and Neural Networks, pages 41–44, 1989.

Martinez, Tony R. ASOCS: a multilayered connectionist network with guaranteed learning of arbitrary mappings. In 2nd IEEE International Conference on Neural Networks, August, August 1988.

Martinez, Tony R. and Vidal, J. J. Adaptive parallel logic networks. Journal of Parallel and Distributed Computing, volume 1, pages 26–58, February 1988.

Martinez, Tony R. On the expedient use of neural networks. volume 1, Neural Networks, S1, p. 552, Presented at the 1st Meeting of the International Neural Network Society, 1988.

Martinez, Tony R. Digital neural networks. In Proceedings of the 1988 IEEE Systems, Man, and Cybernetics Conference, pages 681–684, 1988.

Martinez, Tony R. Models of parallel adaptive logic. In Proceedings of the 1987 IEEE Systems, Man, and Cybernetics Conference, pages 290–296, 1987.

Martinez, Tony R. Adaptive Self-Organizing Logic Networks. technical report, UCLA Technical Report - CSD 860093, June 1986. Ph.D. Dissertation.

Menke, Joshua and Martinez, Tony R. Improving supervised learning by adapting the problem to the learner. In International Journal of Neural Systems, volume 19 (1), pages 1–9, 2009.

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

Menke, Joshua and Martinez, Tony R. Using permutations instead of student’s t distribution for p-values in paired-difference algorithm comparisons.. In Proceedings of the 2004 IEEE Joint Conference on Neural Networks IJCNN’04, 2004.

Menke, Joshua and Martinez, Tony R. Simplifying OCR neural networks through oracle learning. In Proceedings of the 2003 IEEE International Workshop on Soft Computing Techniques in Instrumentation, Measurement, and Related Applications, 2003. IEEE Press.

Menke, Joshua. Neural network simplification through oracle learning. Master’s thesis, Brigham Young University, November 2002.

Menke, Joshua and Peterson, Adam H. and Rimer, Michael E. and Martinez, Tony R. Neural network simplification through oracle learning. In Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’02 , pages 2482–2497, 2002. IEEE Press.

Morring, Brent D. and Martinez, Tony R. Weighted instance typicality search (WITS): a nearest neighbor data reduction algorithm. Intelligent Data Analysis, volume 8 (1), pages 61–78, 2004.

Norton, David and Ventura, Dan. Improving the separability of a reservoir facilitates learning transfer. Proceedings of the International Joint Conference on Neural Networks, pages 2288–2293, 2009.

Norton, David and Ventura, Dan. Preparing more effective liquid state machines using Hebbian learning. In Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’06, pages 8359–8364, 2006.

Peterson, Adam H. COD: measuring the similarity of classifiers. Master’s thesis, Brigham Young University, January 2005.

Peterson, Adam H. and Martinez, Tony R. Estimating the potential for combining learning models. In Proceedings of the ICML Workshop on Meta-Learning, pages 68–75, 2005.

Raykhel, Ilya and Ventura, Dan. Real-time automatic price prediction for ebay online trading. In Proceedings of the Innovative Applications of Artificial Intelligence Conference, pages 135–140, July 2009.

Richards, Mark and Ventura, Dan. Choosing a starting configuration for particle swarm optimization. Proceedings of the Joint Conference on Neural Networks, pages 2309–2312, July 2004.

Richards, Mark and Ventura, Dan. Dynamic sociometry in particle swarm optimization. Proceedings of the Joint Conference on Information Sciences, pages 1557–1560, September 2003.

Rimer, Michael E. and Martinez, Tony R. Classification-based objective functions. Machine Learning, March 2006.

Rimer, Michael E. and Martinez, Tony R. CB3: an adaptive error function for backpropagation training. Neural Processing Letters, volume 24 (1), pages 81–92, 2006.

Rimer, Michael E. and Martinez, Tony R. Softprop: softmax neural network backpropagation learning. In Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’04, pages 979–984, 2004.

Rimer, Michael E. Lazy training: interactive classification learning. Master’s thesis, Brigham Young University, April 2002.

Rimer, Michael E. and Martinez, Tony R. and Wilson, D. Randall. Improving speech recognition learning through lazy training. In Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’02, pages 2568–2573, 2002.

Rimer, Michael E. and Andersen, Tim L. and Martinez, Tony R. Improving backpropagation ensembles through lazy training. In Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’01, pages 2007–2012, 2001.

Rimer, Michael E. and Andersen, Tim L. and Martinez, Tony R. Speed training: improving learning speed for large data sets. In Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’01, pages 2662–2666, 2001.

Rudolph, George L. and Martinez, Tony R. A transformation strategy for implementing distributed multilayer feedfoward networks: backpropagation transformation. Future Generation Computer Systems, volume 6, pages 547–564, 1997.

Rudolph, George L. and Martinez, Tony R. LIA: a location-independent transformation for ASOCS adaptive algorithm 2. International Journal of Neural Systems, 1996.

Rudolph, George L. Location-Independent Neural Network Models. PhD thesis, Brigham Young University, Computer Science Department, August 1995.

Rudolph, George L. and Martinez, Tony R. An efficient transformation for implementing two-layer feedforward neural networks. Journal of Artificial Neural Networks, volume 3, pages 263–282, 1995.

Rudolph, George L. and Martinez, Tony R. A transformation for implementing localist neural networks. Neural Parallel and Scientific Computations, volume 2, pages 173–188, 1995.

Rudolph, George L. and Martinez, Tony R. A transformation for implementing efficient dynamic backpropagation neural networks. In Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pages 41–44, 1995.

Rudolph, George L. and Martinez, Tony R. A transformation for implementing neural networks with localist properties. Yfantis, Evangelos A., editor, Intelligent Systems, volume 1, pages 637–645, Kluwer Academic Publishers, 1995.

Rudolph, George L. and Martinez, Tony R. Location-independent transformations: a general strategy for implementing neural networks. International Journal on Artificial Intelligence Tools, volume 3, pages 417–427, 1994.

Stout, Matthew and Rudolph, George L. and Martinez, Tony R. and Salmon, Linton. A VLSI implementation of a parallel self-organizing learning model. In Proceedings of the 12th International Conference on Pattern Recognition, volume 3, pages 373–376, 1994.

Stout, Matthew and Salmon, Linton and Rudolph, George L. and Martinez, Tony R. A multi-chip module implementation of a neural network. In Proceedings of the IEEE Multi-Chip Module Conference MCMC-94, pages 20–25, 1994.

Rudolph, George L. A location-independent ASOCS model. Master’s thesis, Brigham Young University, June 1991.

Rudolph, George L. and Martinez, Tony R. An efficient static topology for modeling ASOCS. In Kohonen, et. al., editor, Artificial Neural Networks, pages 729–734, 1991. Elsevier Science Publishers.

Rudolph, George L. and Martinez, Tony R. DNA: a new ASOCS model with improved implementation potential. In Proceedings of the IASTED International Symposium on Expert Systems and Neural Networks, pages 12–15, 1989.

Toronto, Neil and Morse, Bryan and Seppi, Kevin and Ventura, Dan. Super-resolution via recapture and bayesian effect modeling. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, page to appear, June 2009.

Toronto, Neil and Morse, Bryan and Ventura, Dan and Seppi, Kevin. The hough transform’s implicit bayesian foundation. In Proceedings of the IEEE International Conference on Image Processing, pages 377–380, September 2007.

Toronto, Neil and Ventura, Dan. Learning quantum operators from quantum state pairs. In IEEE World Congress on Computational Intelligence, pages 2607–2612, July 2006.

Toronto, Neil and Ventura, Dan and Morse, Bryan S. Edge inference for image interpolation. In International Joint Conference on Neural Networks, pages 1782–1787, 2005.

Van Dam, Rob and Geary, Irene and Ventura, Dan. Adapting ADtrees for high arity features. In Proceedings of the Association for the Advancement of Artificial Intelligence, pages 708–713, July 2008.

Van Dam, Rob and Ventura, Dan. ADtrees for sequential data and n-gram counting. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pages 492–497, October 2007.

Hoeffgen, Klaus-Uwe and Simon, Hans-Ulrich and Van Horn, Kevin S. Robust trainability of single neurons. Journal of Computer and System Sciences, volume 50 (1), pages 114–125, 1995.

Van Horn, Kevin S. Learning as Optimization. PhD thesis, Brigham Young University, August 1994.

Van Horn, Kevin S. and Martinez, Tony R. Extending occam’s razor. In Proceedings of the Third Golden West International Conference on Intelligent Systems, Las Vegas, Nevada, June 1994.

Van Horn, Kevin S. and Martinez, Tony R. The minimum feature set problem. Neural Networks , volume 3, pages 491–494, 1994.

Van Horn, Kevin S. and Martinez, Tony R. The BBG rule induction algorithm. In Proceedings of the 6th Australian Joint Conference on Artificial Intelligence, pages 348–355, Melbourne, Australia, November 1993.

Van Horn, Kevin S. and Martinez, Tony R. The Design and Evaluation of a Rule Induction Algorithm. technical report, Technical Report BYU-CS-93-11, November 1993.

Ventura, Dan. “a sub-symbolic model of the cognitive processes of re-representation and insight. In Proceedings of ACM Creativity and Cognition, page to appear, October 2009.

Ventura, Dan. A reductio ad absurdum experiment in sufficiency for evaluating (computational) creative systems. In Proceedings of the International Joint Workshop on Computational Creativity, pages 11–19, September 2008.

Dinerstein, Jonathan and Ventura, Dan and Goodrich, Michael and Egbert, Parris. Data-driven programming and behavior for autonomous virtual characters. In Proceedings of the Association for the Advancement of Artificial Intelligence, pages 1450–1451, July 2008.

Ventura, Dan. Sub-symbolic re-representation to facilitate learning transfer. In Creative Intelligent Systems, AAAI 2008 Spring Symposium Technical Report SS-08-03, pages 128–134, March 2008.

Dinerstein, Sabra and Dinerstein, Jon and Ventura, Dan. Robust multi-modal biometric fusion via SVM ensemble. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pages 1530–1535, October 2007.

Dinerstein, Jonathan and Egbert, Parris and Ventura, Dan. Learning policies for embodied virtual agents through demonstration. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 1257–1262, Hyderabad, India, January 2007.

Merrell, Jake and Ventura, Dan and Morse, Bryan. Clustering music via the temporal similarity of timbre. In IJCAI Workshop on Artificial Intelligence and Music, pages 153–164, January 2007.

Kamali, Kaivan and Ventura, Dan and Garga, Amulya and Kumara, Soundar. Geometric task decomposition in a multi-agent environment. Applied Artificial Intelligence, volume 20 (5), pages 437–456, 2006.

Dinerstein, John and Ventura, Dan and Egbert, Parris. Fast and robust incremental action prediction for interactive agents. Computational Intelligence, volume 21 (1), pages 90–110, 2005.

Ricks, Bob and Ventura, Dan. Training a quantum neural network. In Neural Information Processing Systems, pages 1019–1026, December 2003.

Ventura, Dan. Probabilistic connections in relaxation networks. In Proceedings of the International Joint Conference on Neural Networks, pages 934–938, May 2002.

Ventura, Dan. Pattern classification using a quantum system. In Proceedings of the Joint Conference on Information Sciences, pages 537–640, March 2002.

Ventura, Dan. A quantum analog to basis function networks. In Proceedings of the International Conference on Computing Anticipatory Systems, pages 286–295, August 2001.

Ventura, Dan. On the utility of entanglement in quantum neural computing. In Proceedings of the International Joint Conference on Neural Networks, pages 1565–1570, July 2001.

Ventura, Dan. Learning quantum operators. In Proceedings of the Joint Conference on Information Sciences, pages 750–752, March 2000.

Ezhov, Alexandr and Ventura, Dan. Quantum neural networks. Kasabov, N., editor, Future Directions for Intelligent Systems and Information Science 2000, Physica-Verlag, 2000.

Ezhov, Alexandr and Nifanova, A. and Ventura, Dan. Distributed queries for quantum associative memory. Information Sciences , volume 3-4, pages 271–293, 2000.

Howell, John and Yeazell, John and Ventura, Dan. Optically simulating a quantum associative memory. Physical Review A, volume 62, 2000. Article 42303.

Ventura, Dan and Martinez, Tony R. Quantum associative memory. Information Sciences , volume 1-4, pages 273–296, 2000.

Ventura, Dan and Martinez, Tony R. Initializing the amplitude distribution of a quantum state. Foundations of Physics Letters , volume 6, pages 547–559, December 1999.

Ventura, Dan and Martinez, Tony R. A quantum associative memory based on grover’s algorithm. In Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pages 22–27, April 1999.

Ventura, Dan. Quantum computational intelligence: answers and questions. IEEE Intelligent Systems , volume 4, pages 14–16, 1999.

Ventura, Dan. Implementing competitive learning in a quantum system. In Proceedings of the International Joint Conference on Neural Networks (IJCNN’99), paper 513, 1999.

Ventura, Dan and Wilson, D. Randall and Moncur, Brian and Martinez, Tony R. A neural model of centered tri-gram speech recognition. In Proceedings of the International Joint Conference on Neural Networks (IJCNN’99), paper 2188, 1999.

Ventura, Dan. Artificial associative memory using quantum processes. In Proceedings of the Joint Conference on Information Sciences, volume 2, pages 218–221, October 1998.

Ventura, Dan. Quantum and Evolutionary Approaches to Computational Learning. PhD thesis, Brigham Young University, Computer Science Department, August 1998.

Ventura, Dan and Martinez, Tony R. Quantum associative memory with exponential capacity. In Proceedings of the International Joint Conference on Neural Networks, pages 509–13, May 1998.

Ventura, Dan and Martinez, Tony R. Optimal control using a neural/evolutionary hybrid system. In Proceedings of the International Joint Conference on Neural Networks, pages 1036–41, May 1998.

Ventura, Dan and Martinez, Tony R. Using evolutionary computation to facilitate development of neurocontrol. In Proceedings of the International Workshop on Neural Networks and Neurocontrol, August 1997.

Ventura, Dan and Martinez, Tony R. An artificial neuron with quantum mechanical properties. In Proceedings of the International Conference on Neural Networks and Genetic Algorithms, pages 482–485, 1997.

Ventura, Dan and Martinez, Tony R. Concerning a general framework for the development of intelligent systems. In Proceedings of the IASTED International Conference on Artificial Intelligence, Expert Systems and Neural Networks, pages 44–47, 1996.

Ventura, Dan and Martinez, Tony R. Robust optimization using training set evolution. In Proceedings of the International Conference on Neural Networks, pages 524–8, 1996.

Ventura, Dan and Martinez, Tony R. A general evolutionary/neural hybrid approach to learning optimization problems. In Proceedings of the World Congress on Neural Networks, pages 1091–5, 1996.

Ventura, Dan. On discretization as a preprocessing step for supervised learning models. Master’s thesis, Brigham Young University, Computer Science Department, April 1995.

Ventura, Dan and Andersen, Tim L. and Martinez, Tony R. Using evolutionary computation to generate training set data for neural networks. In Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pages 468–471, 1995.

Ventura, Dan and Martinez, Tony R. An empirical comparison of discretization models. In Proceedings of the 10th International Symposium on Computer and Information Sciences, pages 443–450, 1995.

Ventura, Dan and Martinez, Tony R. Using multiple statistical prototypes to classify continuously valued data. In Proceedings of the International Symposium on Neuroinformatics and Neurocomputers, pages 238–245, 1995.

Ventura, Dan and Martinez, Tony R. BRACE: a paradigm for the discretization of continuously valued data. In Proceedings of the Seventh Florida Artificial Intelligence Research Symposium, pages 117–121, 1994.

Whiting, Stephen and Ventura, Dan. Learning multiple correct classifications from incomplete data using weakened implicit negatives. In Proceedings of the International Joint Conference on Neural Networks, pages 2953–2958, July 2004.

Wilson, D. Randall and Martinez, Tony R. The general inefficiency of batch training for gradient descent learning. Neural Networks, volume 16 (10), pages 1429–1451, Elsevier Science Ltd. Oxford, UK, UK, 2003.

Wilson, D. Randall and Martinez, Tony R. The need for small learning rates on large problems. In Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’01, pages 115–119, 2001.

Wilson, D. Randall and Martinez, Tony R. The inefficiency of batch training for large training sets,. In Proceedings of the International Joint Conference on Neural Networks (IJCNN2000), volume II, pages 113–117, July 2000.

Wilson, D. Randall and Martinez, Tony R. Reduction techniques for exemplar-based learning algorithms. Machine Learning, volume 3, pages 257–286, March 2000.

Wilson, D. Randall and Martinez, Tony R. An integrated instance-based learning algorithm. Computational Intelligence, volume 1, pages 1–28, 2000.

Wilson, D. Randall and Martinez, Tony R. Combining cross-validation and confidence to measure fitness. In Proceedings of the International Joint Conference on Neural Networks (IJCNN’99), paper 163, 1999.

Wilson, D. Randall and Ventura, Dan and Moncur, Brian and Martinez, Tony R. The robustness of relaxation rates in constraint satisfaction networks. In Proceedings of the International Joint Conference on Neural Networks (IJCNN’99), paper 162, 1999.

Wilson, D. Randall. Advances in Instance-Based Learning Algorithms. PhD thesis, Brigham Young University, Computer Science Department, August 1997.

Wilson, D. Randall and Martinez, Tony R. Improved center point selection for probabilistic neural networks. In Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA’97), pages 514–517, 1997.

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