Tony R. Martinez

发表

A VLSI implementation of a parallel, self-organizing learning model

Tony R. Martinez, Matthew G. Stout, Linton G. Salmon, 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 2 - Conference B: Computer Vision & Image Processing. (Cat. No.94CH3440-5).

AN EFFICIENT STATIC TOPOLOGY FOR MODELING ASOCS

Tony R. Martinez, George L. Rudolph, 1991 .

Extending the power and capacity of constraint satisfaction networks

Tony R. Martinez, Xinchuan Zeng, 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

Adaptive Parallel Logic Networks

Tony R. Martinez, Jacques J. Vidal, 1988, J. Parallel Distributed Comput..

Automatic Generation of Music for Inducing Emotive Response

Tony R. Martinez, Dan Ventura, Kristine Monteith, 2010, ICCC.

Recommending Learning Algorithms and Their Associated Hyperparameters

Tony R. Martinez, Michael R. Smith, Logan Mitchell, 2014, MetaSel@ECAI.

A transformation for implementing localist neural networks

Tony R. Martinez, George L. Rudolph, T. Martinez, 1995, Neural Parallel Sci. Comput..

An Integrated Instance‐Based Learning Algorithm

Tony R. Martinez, D. Randall Wilson, T. Martinez, 2000, Comput. Intell..

Domain expert approximation through oracle learning

Tony R. Martinez, Joshua E. Menke, 2005, ESANN.

Detecting Cognitive Distortions Through Machine Learning Text Analytics

Tony R. Martinez, Christophe G. Giraud-Carrier, T. Simms, 2017, 2017 IEEE International Conference on Healthcare Informatics (ICHI).

A Connectionist Method for Adaptive Real-Time Network Routing

Tony R. Martinez, K. McDonald, Douglas M. Campbell, 1991 .

Speed training: improving the rate of backpropagation learning through stochastic sample presentation

Tony R. Martinez, M. E. Rimer, Tim Andersen, 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

AA1*: A Dynamic Incremental Network that Learns by Discrimination

Tony R. Martinez, Christophe G. Giraud-Carrier, T. Martinez, 1995, ICANNGA.

Architecture construction and selection for artificial neural networks

Tony R. Martinez, Timothy Lyon Andersen, 1999 .

A Quantum Associative Memory Based on Grover's Algorithm

Tony R. Martinez, Dan Ventura, T. Martinez, 1999, ICANNGA.

Towards a general distributed platform for learning and generalization

Tony R. Martinez, Brent W. Hughes, 1993, Proceedings 1993 The First New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems.

Using a Neural Network to Approximate an Ensemble of Classifiers

Tony R. Martinez, Xinchuan Zeng, T. Martinez, 2000, Neural Processing Letters.

GS: A Network that Learns Important Features

Tony R. Martinez, Cory Barker, 1993 .

Heterogeneous radial basis function networks

Tony R. Martinez, D. R. Wilson, 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

for Item Recommendation

Tony R. Martinez, Michael R. Smith, Michael S. Gashler, 2015 .

A Learning Model for Adaptive Network Routing

Tony R. Martinez, George L. Rudolph, 1993 .

Generalization By Controlled Intersection Of Examples

Tony R. Martinez, Cory Barker, T. Martinez, 1993 .

An algorithm for correcting mislabeled data

Tony R. Martinez, Xinchuan Zeng, 2001, Intell. Data Anal..

Priority ASOCS

Tony R. Martinez, Douglas M. Campbell, Brent W. Hughes, 1994 .

The minimum feature set problem

Tony R. Martinez, Kevin S. Van Horn, 1994, Neural Networks.

Using multiple statistical prototypes to classify continuously valued data

Tony R. Martinez, Dan Ventura, 1995, The Second International Symposium on Neuroinformatics and Neurocomputers.

Instance-Based Learning with Genetically Derived Attribute Weights

Tony R. Martinez, D. Randall Wilson, 1996 .

A transformation strategy for implementing distributed, multilayer feedforward neural networks: Backpropagation transformation

Tony R. Martinez, George L. Rudolph, T. Martinez, 1997, Future Gener. Comput. Syst..

Quantum associative memory

Tony R. Martinez, Dan Ventura, 2000, Inf. Sci..

R einforcement P rogramming

Tony R. Martinez, George L. Rudolph, Spencer White, 2012 .

A Self-Adjusting Dynamic Logic Module

Tony R. Martinez, Douglas M. Campbell, T. Martinez, 1991, J. Parallel Distributed Comput..

Robust manifold learning with CycleCut

Tony R. Martinez, Michael S. Gashler, T. Martinez, 2012, Connect. Sci..

Turning Bayesian model averaging into Bayesian model combination

Tony R. Martinez, James L. Carroll, Kristine Monteith, 2011, The 2011 International Joint Conference on Neural Networks.

Lazy training: improving backpropagation learning through network interaction

Tony R. Martinez, Tim Andersen, M. E. Rimer, 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

Tangent space guided intelligent neighbor finding

Tony R. Martinez, Michael S. Gashler, 2011, The 2011 International Joint Conference on Neural Networks.

Extending Occam’s Razor

Tony R. Martinez, Kevin S. Van Horn, 1995 .

Improved Center Point Selection for Probabilistic Neural Networks

Tony R. Martinez, D. R. Wilson, T. Martinez, 1997, ICANNGA.

Value Difference Metrics for Continuously Valued Attributes

Tony R. Martinez, D. Randall Wilson, 1996 .

ASOCS: Towards Bridging Neural Network and Artificial Intelligence Learning

Tony R. Martinez, 1991 .

An Incremental Learning Model for Commonsense Reasoning

Tony R. Martinez, Christophe Giraud-Carrier, T. Martinez, 1994 .

Artificial neural network reduction through oracle learning

Tony R. Martinez, Joshua E. Menke, T. Martinez, 2009, Intell. Data Anal..

The Inefficiency of Batch Training for Large Training Sets

Tony R. Martinez, D. Randall Wilson, 2000, IJCNN.

Hierarchical Models for Estimating Individual Ratings from Group Competitions

Tony R. Martinez, Joshua E. Menke, C. Shane Reese, 2007 .

Instance Pruning Techniques

Tony R. Martinez, D. Randall Wilson, T. Martinez, 1997, ICML.

Smart Memory: the Memory Processor Model

Tony R. Martinez, 1990, Modelling the Innovation.

Location-Independent Transformations: a General Strategy for Implementing Neural Networks

Tony R. Martinez, George L. Rudolph, T. Martinez, 1994, Int. J. Artif. Intell. Tools.

Optimal control using a neural/evolutionary hybrid system

Tony R. Martinez, Dan Ventura, 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

CB3: An Adaptive Error Function for Backpropagation Training

Tony R. Martinez, Michael Rimer, 2006, Neural Processing Letters.

Using Evolutionary Computation to Generate Training Set Data for Neural Networks

Tony R. Martinez, Dan Ventura, Tim Andersen, 1995, ICANNGA.

The Potential of Prototype Styles of Generalization

Tony R. Martinez, D. Randall Wilson, T. Martinez, 1993 .

Using Classifier diversity to handle label noise

Tony R. Martinez, Michael R. Smith, T. Martinez, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

LIA: A Location-Independent Transformation for ASOCS Adaptive Algorithm 2

Tony R. Martinez, George L. Rudolph, 1996, Int. J. Neural Syst..

Seven Desirable Properties for Artificial Learning Systems

Tony R. Martinez, Christophe Giraud-Carrier, 1994 .

Finding the Real Differences Between Learning Algorithms

Tony R. Martinez, George L. Rudolph, 2015, Int. J. Artif. Intell. Tools.

On the structure of algorithm spaces

Tony R. Martinez, George L. Rudolph, Adam H. Peterson, 2011, The 2011 International Joint Conference on Neural Networks.

An instance level analysis of data complexity

Tony R. Martinez, Michael R. Smith, Christophe G. Giraud-Carrier, 2014, Machine Learning.

Using multiple measures to predict confidence in instance classification

Tony R. Martinez, Kristine Monteith, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

A Transformation for Implementing Efficient Dynamic Backpropagation Neural Networks

Tony R. Martinez, George L. Rudolph, 1995, ICANNGA.

A neural model of centered tri-gram speech recognition

Tony R. Martinez, Dan Ventura, D. Randall Wilson, 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

The Potential Benefits of Filtering Versus Hyper-Parameter Optimization pdf

Tony R. Martinez, Michael R. Smith, Christophe G. Giraud-Carrier, 2014, ArXiv.

The robustness of relaxation rates in constraint satisfaction networks

Tony R. Martinez, Dan Ventura, D. Randall Wilson, 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

An Artificial Neuron with Quantum Mechanical Properties

Tony R. Martinez, Dan Ventura, T. Martinez, 1997, ICANNGA.

Neural Network Applicability: Classifying the Problem Space

Tony R. Martinez, T. Martinez, 1989 .

The BBG Rule Induction Algorithm

Tony R. Martinez, Kevin S. Van Horn, 1993 .

An Easy to Use Repository for Comparing and Improving Machine Learning Algorithm Usage pdf

Tony R. Martinez, Andrew White, Michael R. Smith, 2014, MetaSel@ECAI.

ILA: Combining Inductive Learning with Prior Knowledge and Reasoning

Tony R. Martinez, Christophe Giraud-Carrier, T. Martinez, 1995 .

The importance of using multiple styles of generalization

Tony R. Martinez, D. Randall Wilson, 1993, Proceedings 1993 The First New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems.

Optimal artificial neural network architecture selection for bagging

Tony R. Martinez, M. E. Rimer, Tim Andersen, 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

Automatic Generation of Melodic Accompaniments for Lyrics

Tony R. Martinez, Dan Ventura, Kristine Monteith, 2012, ICCC.

Using learning algorithm behavior to chart task space: The DICES distance

Tony R. Martinez, Adam H. Peterson, T. Martinez, 2010, Intell. Data Anal..

Using Multiple Node Types to Improve the Performance of DMP

Tony R. Martinez, Theodore Lynde Anderson, 1996 .

An efficient transformation for implementing two-layer feedforward neural networks

Tony R. Martinez, George L. Rudolph, 1995 .

Rescaling the energy function in Hopfield networks

Tony R. Martinez, Xinchuan Zeng, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

A noise filtering method using neural networks

Tony R. Martinez, Xinchuan Zeng, T. Martinez, 2003, IEEE International Workshop on Soft Computing Techniques in Instrumentation, Measurement and Related Applications, 2003. SCIMA 2003..

The need for small learning rates on large problems

Tony R. Martinez, D. R. Wilson, T. Martinez, 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

Network simplification through oracle learning

Tony R. Martinez, Joshua E. Menke, M. E. Rimer, 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

Analysis of the Convergence and Generalization of AA1

Tony R. Martinez, Christophe G. Giraud-Carrier, T. Martinez, 1995, J. Parallel Distributed Comput..

Generating a novel sort algorithm using Reinforcement Programming

Tony R. Martinez, George L. Rudolph, Spencer K. White, 2010, IEEE Congress on Evolutionary Computation.

Pair attribute learning: network construction using pair features

Tony R. Martinez, E. K. Henderson, T. Martinez, 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

A New Activation Function in the Hopfield Network for Solving Optimization Problems

Tony R. Martinez, Xinchuan Zeng, 1999, ICANNGA.

Improving speech recognition learning through lazy training

Tony R. Martinez, M. E. Rimer, D. R. Wilson, 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

An Integrated Framework for Learning and Reasoning pdf

Tony R. Martinez, Christophe G. Giraud-Carrier, T. Martinez, 1995, J. Artif. Intell. Res..

Neural networks and machine learning: towards fully automated learning

Tony R. Martinez, 2004, Proceedings of the Fifth Mexican International Conference in Computer Science, 2004. ENC 2004..

The robustness of majority voting compared to filtering misclassified instances in supervised classification tasks

Tony R. Martinez, Michael R. Smith, T. Martinez, 2016, Artificial Intelligence Review.

Distribution-balanced stratified cross-validation for accuracy estimation

Tony R. Martinez, Xinchuan Zeng, T. Martinez, 2000, J. Exp. Theor. Artif. Intell..

The Effect of Decision Surface Fitness on Dynamic Multilayer Perceptron Networks (DMP1)

Tony R. Martinez, Theodore Lynde Anderson, 1996 .

Decision Tree Ensemble: Small Heterogeneous Is Better Than Large Homogeneous

Tony R. Martinez, Christophe G. Giraud-Carrier, Michael S. Gashler, 2008, 2008 Seventh International Conference on Machine Learning and Applications.

Constructing Low-Order Discriminant Neural Networks Using Statistical Feature Selection

Tony R. Martinez, E. K. Henderson, T. Martinez, 2007 .

An Extensive Evaluation of Filtering Misclassified Instances in Supervised Classification Tasks

Tony R. Martinez, Michael R. Smith, T. Martinez, 2013, ArXiv.

A Hierarchical Multi-Output Nearest Neighbor Model for Multi-Output Dependence Learning pdf

Tony R. Martinez, Michael R. Smith, Richard G. Morris, 2014, ArXiv.

Robust optimization using training set evolution

Tony R. Martinez, Dan Ventura, 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

A multi-chip module implementation of a neural network

Tony R. Martinez, George L. Rudolph, M. G. Stout, 1994, Proceedings of IEEE Multi-Chip Module Conference (MCMC-94).

Temporal nonlinear dimensionality reduction

Tony R. Martinez, Michael S. Gashler, 2011, The 2011 International Joint Conference on Neural Networks.

Reducing Decision Tree Ensemble Size Using Parallel Decision DAGs

Tony R. Martinez, Adam H. Peterson, T. Martinez, 2009, Int. J. Artif. Intell. Tools.

Combining cross-validation and confidence to measure fitness

Tony R. Martinez, D. Randall Wilson, T. Martinez, 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

Adaptive self-organizing logic networks

Tony R. Martinez, T. Martinez, 1986 .

AUTOMATIC ALGORITHM DEVELOPMENT USING NEW REINFORCEMENT PROGRAMMING TECHNIQUES

Tony R. Martinez, George L. Rudolph, Spencer K. White, 2012, Comput. Intell..

A Bradley–Terry artificial neural network model for individual ratings in group competitions

Tony R. Martinez, Joshua E. Menke, T. Martinez, 2008, Neural Computing and Applications.

Weighted Instance Typicality Search (WITS): A nearest neighbor data reduction algorithm

Tony R. Martinez, Brent D. Morring, T. Martinez, 2004, Intell. Data Anal..

The general inefficiency of batch training for gradient descent learning

Tony R. Martinez, D. Randall Wilson, T. Martinez, 2003, Neural Networks.

Improved Backpropagation Learning in Neural Networks with Windowed Momentum

Tony R. Martinez, Ernest Istook, T. Martinez, 2002, Int. J. Neural Syst..

Iterative Non-linear Dimensionality Reduction with Manifold Sculpting

Tony R. Martinez, Dan Ventura, Michael S. Gashler, 2007, NIPS.

Improving classification accuracy by identifying and removing instances that should be misclassified

Tony R. Martinez, Michael R. Smith, T. Martinez, 2011, The 2011 International Joint Conference on Neural Networks.

Digital Neural Networks

Tony R. Martinez, T. Martinez, 1988, Proceedings of the 1988 IEEE International Conference on Systems, Man, and Cybernetics.

Improved Heterogeneous Distance Functions

Tony R. Martinez, D. Randall Wilson, T. Martinez, 1996, J. Artif. Intell. Res..

A self-organizing binary decision tree for incrementally defined rule-based systems

Tony R. Martinez, Douglas M. Campbell, 1991, IEEE Trans. Syst. Man Cybern..

Simplifying OCR neural networks with oracle learning

Tony R. Martinez, Joshua E. Menke, 2003, IEEE International Workshop on Soft Computing Techniques in Instrumentation, Measurement and Related Applications, 2003. SCIMA 2003..

BRACE: A Paradigm For the Discretization of Continuously Valued Data

Tony R. Martinez, Dan Ventura, T. Martinez, 1994 .

Missing Value Imputation with Unsupervised Backpropagation

Tony R. Martinez, Michael R. Smith, Michael S. Gashler, 2013, Comput. Intell..

AGGREGATE CERTAINTY ESTIMATORS

Tony R. Martinez, Kristine Monteith, T. Martinez, 2013, Comput. Intell..

A hybrid latent variable neural network model for item recommendation

Tony R. Martinez, Michael R. Smith, Michael S. Gashler, 2014, 2015 International Joint Conference on Neural Networks (IJCNN).

Efficient Construction of Networks for Learned Representations with General to Specific Relationships

Tony R. Martinez, Cory Barker, 1995 .

The Potential Benefits of Data Set Filtering and Learning Algorithm Hyperparameter Optimization

Tony R. Martinez, Michael R. Smith, Christophe G. Giraud-Carrier, 2015, MetaSel@PKDD/ECML.

DMP3: A Dynamic Multilayer Perceptron Construction Algorithm

Tony R. Martinez, Timothy L. Andersen, T. Martinez, 2001, Int. J. Neural Syst..

Improving the Performance of the Hopfield Network By Using A Relaxation Rate

Tony R. Martinez, Xinchuan Zeng, 1999, ICANNGA.

Proof of correctness for ASOCS AA3 networks

Tony R. Martinez, J. C. Barker, T. Martinez, 1994, IEEE Trans. Syst. Man Cybern..

Automatic Generation of Music for Inducing Physiological Response

Tony R. Martinez, Dan Ventura, Kristine Monteith, 2013, CogSci.

Concerning a General Framework for the Development of Intelligent Systems

Tony R. Martinez, Dan Ventura, T. Martinez, 1996 .

Learning by Discrimination: A Constructive Incremental Approach

Tony R. Martinez, Christophe G. Giraud-Carrier, T. Martinez, 2007, J. Comput..

Classification-based objective functions

Tony R. Martinez, Michael Rimer, T. Martinez, 2006, Machine Learning.

Using Decision Trees and Soft Labeling to Filter Mislabeled Data

Tony R. Martinez, Xinchuan Zeng, 2008 .

Learning and Generalization Controlled by Contradiction

Tony R. Martinez, Cory Barker, 1993 .

Improving Supervised Learning by Adapting the Problem to the Learner

Tony R. Martinez, Joshua E. Menke, T. Martinez, 2009, Int. J. Neural Syst..

Improved Hopfield networks by training with noisy data

Tony R. Martinez, F. Clift, F. Clift, 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

A Proposed Universal Reference Code

Tony R. Martinez, T. Martinez, 1987 .

Consistency and generalization in incrementally trained connectionist networks

Tony R. Martinez, 1990, IEEE International Symposium on Circuits and Systems.

Hopfield networks and optimization

Tony R. Martinez, Xinchuan Zeng, 2005 .

Reduction Techniques for Instance-Based Learning Algorithms

Tony R. Martinez, D. Randall Wilson, R. Holte, 2000, Machine Learning.

Automatic Generation of Emotionally-Targeted Soundtracks

Tony R. Martinez, Dan Ventura, Virginia Francisco, 2011, ICCC.

Manifold Learning by Graduated Optimization

Tony R. Martinez, Dan Ventura, Michael S. Gashler, 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

Adaptive Self-Organizing Concurrent Systems

Tony R. Martinez, T. Martinez, 1990 .

Bias and the probability of generalization

Tony R. Martinez, D. R. Wilson, T. Martinez, 1997, Proceedings Intelligent Information Systems. IIS'97.

A New Relaxation Procedure in the Hopfield Network for Solving Optimization Problems

Tony R. Martinez, Xinchuan Zeng, T. Martinez, 1999, Neural Processing Letters.

Using precepts to augment training set learning

Tony R. Martinez, Christophe G. Giraud-Carrier, 1993, Proceedings 1993 The First New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems.

Reducing the Effects of Detrimental Instances

Tony R. Martinez, Michael R. Smith, T. Martinez, 2014, 2014 13th International Conference on Machine Learning and Applications.

Becoming More Robust to Label Noise with Classifier Diversity pdf

Tony R. Martinez, Michael R. Smith, T. Martinez, 2014, ArXiv.

An Empirical Comparison of Discretization Methods

Tony R. Martinez, Dan Ventura, 1995 .

Generating three binary addition algorithms using reinforcement programming

Tony R. Martinez, George L. Rudolph, Spencer K. White, 2010, ACM SE '10.

Improving the Performance of the Hopfield Network for Solving Optimization Problems Abstract Improving the Performance of the Hopfield Network for Solving Optimization Problems

Tony R. Martinez, Xinchuan Zeng, 1998 .

A General Evolutionary/Neural Hybrid Approach to Learning Optimization Problems

Tony R. Martinez, Dan Ventura, 1996 .