Michael T. Manry

发表

New learning factor and testing methods for conjugate gradient training algorithm

Tae Kim, Michael T. Manry, J. Maldonado, 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

Shape recognition with nearest neighbor isomorphic network

Michael T. Manry, H.-C. Yau, 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

Analysis and improvement of multiple optimal learning factors for feed-forward networks

Michael T. Manry, Praveen Jesudhas, Rohit Rawat, 2011, The 2011 International Joint Conference on Neural Networks.

Demodulation for wireless ATM network using modified SOM network

Michael T. Manry, Jiang Li, Qilian Liang, 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

Signal Processing Using the Multilayer Perceptron

Michael T. Manry, Hema Chandrasekaran, Cheng-Hsiung Hsieh, 2001, Handbook of Neural Network Signal Processing.

Convergent design of a piecewise linear neural network

Michael T. Manry, Hema Chandrasekaran, M. Manry, 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

Iterative improvement of a nearest neighbor classifier

Michael T. Manry, Hung-Chun Yau, M. Manry, 1991, Neural Networks.

A semiblind demodulator aided by protocols for wireless ATM network

Michael T. Manry, Jiang Li, Qilian Liang, 2003, 14th IEEE Proceedings on Personal, Indoor and Mobile Radio Communications, 2003. PIMRC 2003..

An integrated growing-pruning method for feedforward network training

Michael T. Manry, Jiang Li, Pramod Lakshmi Narasimha, 2008, Neurocomputing.

A Functional Link Network With Ordered Basis Functions

Michael T. Manry, Saurabh Sureka, 2007, 2007 International Joint Conference on Neural Networks.

Multi-variable Neural Network Forecasting Using Two Stage Feature Selection

Michael T. Manry, Rohit Rawat, Kunal Vora, 2014, 2014 13th International Conference on Machine Learning and Applications.

Fast training of neural networks for remote sensing

Michael T. Manry, Adrian K. Fung, M. S. Dawson, 1994 .

A neural network training algorithm utilizing multiple sets of linear equations

Michael T. Manry, Hung-Han Chen, 1996 .

Sigma-pi implementation of a Gaussian classifier

Michael T. Manry, Hung-Chau Yau, 1990, 1990 IJCNN International Joint Conference on Neural Networks.

Parameter estimation algorithms utilizing implicit phase

Michael T. Manry, 1984, ICASSP.

Multiple optimal learning factors for feed-forward networks

Michael T. Manry, Sanjeev S. Malalur, M. Manry, 2010, Defense + Commercial Sensing.

Reservoir inflow forecasting using neural networks

Michael T. Manry, Chandrashekar Subramanian, Jorge Naccarino, 1999 .

Compact polynomial modeling of the multi-layer perceptron

Michael T. Manry, X. Jiang, Mu-Song Chen, 1992, [1992] Conference Record of the Twenty-Sixth Asilomar Conference on Signals, Systems & Computers.

A neural network training algorithm utilizing multiple sets of linear equations

Michael T. Manry, Hema Chandrasekaran, Hung-Han Chen, 1996, Conference Record of The Thirtieth Asilomar Conference on Signals, Systems and Computers.

Surface parameter retrieval using fast learning neural networks

Michael T. Manry, M. S. Dawson, Adrian K. Fung, 1993 .

Neural decision directed segmentation of silicon defects

Michael T. Manry, Aditi S. Godbole, Kanishka Tyagi, 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

Power series analyses of back-propagation neural networks

Michael T. Manry, Mu-Song Chen, 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

Fuzzy C-means clustering based construction and training for second order RBF network

Michael T. Manry, Xun Cai, Kanishka Tyagi, 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

Investigation of human frontal cortex under noxious thermal stimulation of temporo-mandibular joint using functional near infrared spectroscopy

Michael T. Manry, Rohit Rawat, Hanli Liu, 2013, Photonics West - Biomedical Optics.

Structural adaptation for sparsely connected MLP using Newton's method

Michael T. Manry, Son Nguyen, Kanishka Tyagi, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

Progressive image transmission using a self-supervised back-propagation neural network

Michael T. Manry, Wei Gong, K. R. Rao, 1992, J. Electronic Imaging.

Basis vector analyses of back-propagation neural networks

Michael T. Manry, Mu-Song Chen, M. Manry, 1991, [1991] Proceedings of the 34th Midwest Symposium on Circuits and Systems.

Second order design of multiclass kernel machines

Yilong Hao, Michael T. Manry, Rohit Rawat, 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

Attributes of neural networks for extracting continuous vegetation variables from optical and radar

Michael T. Manry, Adrian K. Fung, Ross Nelson, 1998 .

Prototype Based Classifier Design with Pruning

Michael T. Manry, Jiang Li, Changhua Yu, 2004, FLAIRS.

A frequency domain weighting function approach to extrapolation

Michael T. Manry, P. J. Thompson, 1990, IEEE Trans. Acoust. Speech Signal Process..

Training multilayer perceptron by using optimal input normalization

Michael T. Manry, Xun Cai, Kanishka Tyagi, 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

Conventional modeling of the multilayer perceptron using polynomial basis functions

Michael T. Manry, Mu-Song Chen, 1993, IEEE Trans. Neural Networks.

Overcoming co-channel interference in TDMA systems using SOM equalizer

Michael T. Manry, Jiang Li, Qilian Liang, 2003, Radio and Wireless Conference, 2003. RAWCON '03. Proceedings.

Analysis and optimization of neural networks for remote sensing

Michael T. Manry, M. S. Dawson, Mu-Song Chen, 1994 .

An Efficient Feature Selection Algorithm for Computer-aided Polyp Detection

Ronald M. Summers, Michael T. Manry, Nicholas Petrick, 2006, Int. J. Artif. Intell. Tools.

Progressive image transmission using self-supervised backpropagation neural network

Michael T. Manry, K. R. Rao, W. Gong, 1991, [1991] Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems & Computers.

Multilayer neural network design based on a modular concept

Michael T. Manry, Kamyar Rohani, 1994 .

Transform-based shape recognition employing neural networks

Michael T. Manry, Hung-Chun Yau, 1990 .

Sizing of the multilayer perceptron via modular networks

Michael T. Manry, Hema Chandrasekaran, K. K. Kim, 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

A nonlinear filtering approach for demodulation over Rician flat fading channels

Michael T. Manry, Jiang Li, Qilian Liang, 2004, IEEE Global Telecommunications Conference, 2004. GLOBECOM '04..

Taylor series approximation of the energy spectral density

Michael T. Manry, Rashid Zia Khan, Lars A. Anderson, 1988, IEEE Trans. Acoust. Speech Signal Process..

An optimal construction and training of second order RBF network for approximation and illumination invariant image segmentation

Michael T. Manry, Xun Cai, Kanishka Tyagi, 2011, The 2011 International Joint Conference on Neural Networks.

An efficient conjugate gradient based learning algorithm for multiple optimal learning factors of multilayer perceptron neural network

Zhi Chen, Michael T. Manry, Xun Cai, 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

A robust statistical-based estimator for soil moisture retrieval from radar measurements

Michael T. Manry, Adrian K. Fung, Michael S. Dawson, 1997, IEEE Trans. Geosci. Remote. Sens..

Bounding the performance of neural network estimators, given only a set of training data

Michael T. Manry, A. K. Fung, Qiang Yu, 1994, Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers.

Finding optimal neural network basis function subsets using the Schmidt procedure

Michael T. Manry, Francisco Maldonado, Tae-Hoon Kim, 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

Feature Selection Using a Piecewise Linear Network

Michael T. Manry, Jiang Li, Pramod Lakshmi Narasimha, 2006, IEEE Transactions on Neural Networks.

Adaptive channel equalization for satellite communications with multipath based on unsupervised learning algorithm

Michael T. Manry, Jiang Li, Qilian Liang, 2003, 14th IEEE Proceedings on Personal, Indoor and Mobile Radio Communications, 2003. PIMRC 2003..

Backpropagation representation theorem using power series

Michael T. Manry, Mu-Song Chen, 1990, 1990 IJCNN International Joint Conference on Neural Networks.

A building block approach for analysis and design of nonlinear neural network filters

Michael T. Manry, Kamyar Rohani, 1991 .

Co-channel interference suppression with model simplification in TDMA systems

Michael T. Manry, Jiang Li, Qilian Liang, 2004, 2004 IEEE 15th International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE Cat. No.04TH8754).

A new signal averaging algorithm for discrete signals

Michael T. Manry, Chun-Chau Lin, 1989, IEEE Trans. Acoust. Speech Signal Process..

Image coding using self-supervised backpropagation neural network

Michael T. Manry, K. R. Rao, W. Gong, 1991, [1991] Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems & Computers.

Sensitivity of Nonlinear Network Training to Affine Transformed Inputs

Michael T. Manry, Pramod Lakshmi Narasimha, Changhua Yu, 2005, FLAIRS Conference.

Vector quantization and progressive image transmission using Kohonen self-organizing feature map

Michael T. Manry, K. R. Rao, W. Gong, 1991, [1991] Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems & Computers.

Iterative Design of Neural Network Classifiers Through Regression

Michael T. Manry, Jiang Li, Li-min Liu, 2005, Int. J. Artif. Intell. Tools.

Automatic recognition of USGS land use/cover categories using statistical and neural network classifiers

Michael T. Manry, Robert R. Bailey, Xianping Jiang, 1993, Defense, Security, and Sensing.

Multiple optimal learning factors for the multi-layer perceptron

Michael T. Manry, Praveen Jesudhas, Sanjeev S. Malalur, 2015, Neurocomputing.

The design of multi-layer perceptrons using building blocks

Michael T. Manry, K. Rohani, 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

Upper Bound on Pattern Storage in Feedforward Networks

Michael T. Manry, Pramod Lakshmi Narasimha, Francisco Maldonado, 2007, 2007 International Joint Conference on Neural Networks.

Sigma-pi implementation of a nearest neighbor classifier

Michael T. Manry, Hung-Chun Yau, 1990, 1990 IJCNN International Joint Conference on Neural Networks.

Optimal Conjugate Gradient Algorithm for Generalization of Linear Discriminant Analysis Based on L1 Norm

Michael T. Manry, Nojun Kwak, Kanishka Tyagi, 2014, ICPRAM.

Iterative improvement of a Gaussian classifier

Michael T. Manry, Hung-Chun Yau, 1990, Neural Networks.

LMS learning algorithms: misconceptions and new results on converence

Michael T. Manry, Zi-Qin Wang, Jeffrey L. Schiano, 2000, IEEE Trans. Neural Networks Learn. Syst..

Invariance of MLP Training to Input Feature De-correlation

Michael T. Manry, Jiang Li, Changhua Yu, 2004, FLAIRS Conference.

Comparisons of a neural network and a nearest-neighbor classifier via the numeric handprint recognition problem

Michael T. Manry, Wei Gong, Hung-Chun Yau, 1995, IEEE Trans. Neural Networks.

Upper bound on pattern storage in feedforward networks

Michael T. Manry, Pramod Lakshmi Narasimha, Francisco Maldonado, 2008, Neurocomputing.

Calculation of Cramer Rao maximum a posteriori lower bounds from training data

Michael T. Manry, Cheng-Hsiung Hsieh, Ting-Cheng Chang, 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

Properties of a Batch Training Algorithm for Feedforward Networks

Michael T. Manry, Sanjeev S. Malalur, Changhua Yu, 2017, Neural Processing Letters.

An automated system for developing neural network short term load forecasters

Michael T. Manry, Jorge Naccarino, Ray Shoults, 1996 .

Recent Developments in Multilayer Perceptron Neural Networks

Michael T. Manry, Walter H. Delashmit, Lockheed Martin Missiles, 2005 .

Discriminant vector tranformations in neural network classifiers

Michael T. Manry, Son Nguyen, Kanishka Tyagi, 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

Neural subnet design by direct polynomial mapping

Michael T. Manry, Mu-Song Chen, Kamyar Rohani, 1992, IEEE Trans. Neural Networks.

Output weight optimization for the multi-layer perceptron

Michael T. Manry, Steven J. Apollo, L. S. Allen, 1992, [1992] Conference Record of the Twenty-Sixth Asilomar Conference on Signals, Systems & Computers.

Partially affine invariant back propagation

Michael T. Manry, Son Nguyen, Kanishka Tyagi, 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

Pseudogenetic algorithms for mlp design based upon schmidt procedure

Michael T. Manry, Francisco Maldonado, 2002 .

Convergent design of piecewise linear neural networks

Michael T. Manry, Jiang Li, Hema Chandrasekaran, 2007, Neurocomputing.

Minimum mean square estimation and neural networks

Michael T. Manry, Qiang Yu, Steven J. Apollo, 1996, Neurocomputing.

A Piecewise Linear Network Classifier

Michael T. Manry, Jiang Li, Sanjeev S. Malalur, 2007, 2007 International Joint Conference on Neural Networks.

Fast Generation of a Sequence of Trained and Validated Feed-Forward Networks

Michael T. Manry, Jiang Li, Pramod Lakshmi Narasimha, 2006, FLAIRS Conference.

A Pseudogenetic Algorithm for MLP Design based upon the Schmidt Procedure

Michael T. Manry, F. J. Maldonado, 2003, Neural Networks and Computational Intelligence.

Phase improvement in IIR digital filters

Michael T. Manry, Songqing Shi, 1989, IEEE Trans. Acoust. Speech Signal Process..

Feed-forward network training using optimal input gains

Michael T. Manry, Sanjeev S. Malalur, M. Manry, 2009, 2009 International Joint Conference on Neural Networks.

Optimal output gain algorithm for feed-forward network training

Michael T. Manry, Rohit Rawat, Babu Hemanth Kumar Aswathappa, 2011, The 2011 International Joint Conference on Neural Networks.

Stochastic Cramer Rao bounds for non-Gaussian signals and parameters

Michael T. Manry, Adrian K. Fung, Michael S. Dawson, 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

Iterative Improvement of Neural Classifiers

Michael T. Manry, Jiang Li, Li-min Liu, 2004, FLAIRS.

Neural networks: Algorithms, applications, and programming techniques: By James A. Freeman and David M. Skapura, Addison-Wesley Publishing, Reading, MA, ISBN 0-201-51376-5

Michael T. Manry, M. Manry, 1994 .

Prototype Classifier Design with Pruning

Michael T. Manry, Jiang Li, D. Randall Wilson, 2005, Int. J. Artif. Intell. Tools.

Comparison of very short-term load forecasting techniques

Michael T. Manry, Chiman Kwan, R. R. Shoults, 1996 .

An efficient hidden layer training method for the multilayer perceptron

Michael T. Manry, Jiang Li, Pramod Lakshmi Narasimha, 2006, Neurocomputing.

Two-Stage Second Order Training in Feedforward Neural Networks

Michael T. Manry, Melvin Deloyd Robinson, M. Manry, 2013, FLAIRS.

Adaptive pattern recognition and neural networks: By Yoh-Han Pao, Addison-Wesley Publishing Company, Inc., New York: 1989, $40.76, 309 pp. ISBN 0-201-12584-6

Michael T. Manry, M. Manry, 1991 .

Partially affine invariant training using dense transform matrices

Michael T. Manry, Melvin Deloyd Robinson, M. Manry, 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

Classification-based segmentation of ZIP codes

Michael T. Manry, Yousuf Saifullah, 1993, IEEE Trans. Syst. Man Cybern..

Nonlinear Network Time-Series Forecasting Using Redundancy Reduction

Michael T. Manry, Pramod Lakshmi Narasimha, Changhua Yu, 2005, FLAIRS Conference.

Iterative Improvement of trigonometric networks

Michael T. Manry, Hema Chandrasekaran, Iyab I. Sakhnini, 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

Pruning of basis functions in nonlinear approximators

Michael T. Manry, Hema Chandrasekaran, Hung-Han Chen, 2000, Neurocomputing.

Small Models of Large Machines

Michael T. Manry, Sanjeev S. Malalur, Pramod Lakshmi Narasimha, 2008, FLAIRS Conference.

Hidden Layer Training via Hessian Matrix Information

Michael T. Manry, Jiang Li, Changhua Yu, 2004, FLAIRS.

Minimizing validation error with respect to network size and number of training epochs

Michael T. Manry, Rohit Rawat, Jignesh K. Patel, 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

Recursive approximation of the energy spectral density

Michael T. Manry, Hung-Chun Yau, Lars A. Anderson, 1992, IEEE Trans. Signal Process..

Second Order Training of a Smoothed Piecewise Linear Network

Michael T. Manry, Rohit Rawat, M. Manry, 2017, Neural Processing Letters.

Progressive image transmission

Michael T. Manry, Wei Gong, K. R. Rao, 1993, IEEE Trans. Circuits Syst. Video Technol..

Real-time object-based image registration using improved MRAN

Michael T. Manry, Kamesh Subbarao, Zhanfeng Yue, 2007, SPIE Defense + Commercial Sensing.

Near-optimal flight load synthesis using neural nets

Michael T. Manry, Hema Chandrasekaran, Cheng-Hsiung Hsieh, 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

Balanced Gradient Training of Feed Forward Networks

Michael T. Manry, Son Nguyen, M. Manry, 2021, Neural Processing Letters.

Multistage Newton’s Approach for Training Radial Basis Function Neural Networks

Michael T. Manry, Kanishka Tyagi, Chinmay Rane, 2021, SN Computer Science.

Cramer Rao Maximum A-Posteriori Bounds on Neural Network Training Error for Non-Gaussian Signals and Parameters

Michael T. Manry, Cheng-Hsiung Hsieh, Adrian K. Fung, 1996 .

A mapping approach for designing neural sub-nets

Michael T. Manry, Mu-Song Chen, K. Rohani, 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

A Preliminary Investigation of Human Frontal Cortex Under Noxious Thermal Stimulation Over the Temporomandibular Joint Using Functional Near Infrared Spectroscopy

Michael T. Manry, Hanli Liu, Rohit Rawat, 2013 .

Inversion of Surface Parameters Using Fast Learning Neural Networks

Michael T. Manry, Adrian K. Fung, M. S. Dawson, 1992, [Proceedings] IGARSS '92 International Geoscience and Remote Sensing Symposium.