Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovering novel fighter combat maneuvers: simulating test pilot creativity
Creative evolutionary systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Zcs: A zeroth level classifier system
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
Patterns in pattern recognition: 1968-1974
IEEE Transactions on Information Theory
A novel conflict reassignment method based on grey relational analysis (GRA)
Pattern Recognition Letters
PCA for improving the performance of XCSR in classification of high-dimensional problems
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
XCS-based versus UCS-based feature pattern classification system
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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This work developed and demonstrated a machine learning approach for robust ATR. The primary innovation of this work was the development of an automated way of developing inference rules that can draw on multiple models and multiple feature types to make robust ATR decisions. The key realization is that this ''meta learning'' problem is one of structural learning, and that it can be conducted independently of parameter learning associated with each model and feature based technique. This was accomplished by using a learning classifier system, which is based on genetics-based machine learning, for the ill conditioned combinatorial problem of structural rule learning, while using statistical and mathematical techniques for parameter learning. This system was tested on MSTAR Public Release SAR data using standard and extended operation conditions. These results were also compared against two baseline classifiers, a PCA based distance classifier and a MSE classifier. The classifiers were evaluated for accuracy (via training set classification) and robustness (via testing set classification). In both cases, the LCS based robust ATR system performed well with accuracy over 99% and robustness over 80%.