P. Pachowicz

发表

Competitive reinforcement learning in continuous control tasks

H. Wechsler, M. Abramson, P. Pachowicz, 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

Local Characteristics of Bi~ary I\l\ges A~d Their Application to the Automatic C01\trol of Lo\v -level Robot Visio~

P. Pachowicz, 2009 .

OPTIMIZATION OF CONCEPT PROTOTYPES FOR THE RECOGNITION OF NOISY TEXTURE DATA

P. Pachowicz, J. Bala, 1991 .

The Monk''s Problem''s: A Performance Comparison of Different Learning Methods

Ivan Bratko, Peter W. Pachowicz, Sebastian Thrun, 1991 .

Recognizing and Incrementally Evolving Texture Concepts in Dynamic Environments : An Incremental Model Generalization Approach

P. Pachowicz, 1991 .

A NOISE-TOLERANT

P. Pachowicz, J. Bala, 2022 .

RECOGNIZING NOISY PATTERNS OF TEXTURE VIA ITERATIVE OPI'IMIZATION AND MATCHING OF THEIR RULE DESCRIPTION

P. Pachowicz, 2009 .

A Lzarhing-based Nlcrzhehtal Model Evolution Fon Nrv~n..il'..ltt Object Recognition

P. Pachowicz, M. Bieb, Pervez Mobta, 2022 .

GMU Research on Learning in Vision: Initial Results

Peter W. Pachowicz, Ryszard S. Michalski, Jerzy Bala, 1993 .

Machine Learning and Vision: Research Issues and Promising Directions

Azriel Rosenfeld, Peter W. Pachowicz, Ryszard S. Michalski, 1993 .

A Brief Review of AQ Learning Programs and Their Application to the MONKS Problems

Peter W. Pachowicz, Kenneth A. Kaufman, Ryszard S. Michalski, 1992 .

Progress on Vision Through Learning at George Mason University

Peter W. Pachowicz, Ryszard S. Michalski, Jerzy Bala, 1994 .

Semi-Autonomous Evolution of Object Models for Adaptive Object Recognition

P. W. Pachowicz, P. Pachowicz, 1994, IEEE Trans. Syst. Man Cybern. Syst..

A Noise-Tolerant Approach to Symbolic Learning from Sensory Data

Jerzy W. Bala, Peter W. Pachowicz, P. Pachowicz, 1994, J. Intell. Fuzzy Syst..

Application of symbolic machine learning to the recognition of texture concepts

P. W. Pachowicz, J. W. Bala, P. Pachowicz, 1991, [1991] Proceedings. The Seventh IEEE Conference on Artificial Intelligence Application.

Integrating Low-Level Features Computation with Inductive Learning Techniques for Texture Recognition

Peter W. Pachowicz, P. Pachowicz, 1990, Int. J. Pattern Recognit. Artif. Intell..

Low-level numerical characteristics and inductive learning methodology in texture recognition

Peter W. Pachowicz, P. Pachowicz, 1989, [Proceedings 1989] IEEE International Workshop on Tools for Artificial Intelligence.

On-Line Model Modification for Adaptive Object Recognition

Sung Wook Baik, Peter W. Pachowicz, S. Baik, 2000, ECAI.

Adaptive RBF classifier for object recognition in image sequences

Sung Wook Baik, Peter W. Pachowicz, S. Baik, 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.

Genetic Evolution Approach for Target Movement Prediction

Sung Wook Baik, Jerzy W. Bala, Ali Hadjarian, 2004, International Conference on Computational Science.

Visualization for Genetic Evolution of Target Movement in Battle Fields

S. Baik, P. Pachowicz, J. Bala, 2005, International Conference on Computational Science.

Moving Target Prediction Using Evolutionary Algorithms

Sung Wook Baik, Jerzy W. Bala, Ali Hadjarian, 2005, Canadian Conference on AI.

Improving Recognition Effectiveness of Noisy Texture Concepts

Jerzy W. Bala, Peter W. Pachowicz, P. Pachowicz, 1991, ML.

Online model modification for adaptive texture recognition in image sequences

Sung Wook Baik, Peter W. Pachowicz, S. Baik, 2002, IEEE Trans. Syst. Man Cybern. Part A.

Computational Modeling of Cultural Dimensions in Adversary Organizations

Peter W. Pachowicz, Alexander H. Levis, Kathleen M. Carley, 2010 .

TEXTURE RECOGNITION THROUGH MACHINE LEARNING AND CONCEPT OPTIMIZIATION

P. W. Pachowicz, P. Pachowicz, J. W. Bata, 2009 .

Learning-based architecture for robust recognition of variable texture to navigate in natural terrain

Peter W. Pachowicz, P. Pachowicz, 1990, EEE International Workshop on Intelligent Robots and Systems, Towards a New Frontier of Applications.

LEARNING INVARIANT TEXTURE CHARACTERISTICS IN DYNAMIC ENVIRONMENTS : A model evolution approach

Peter W. Pachowicz, P. Pachowicz, 1991 .

Image processing and analysis of the grain constitution in minerals

Peter W. Pachowicz, P. Pachowicz, 1984, Image Vis. Comput..