Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Machine Learning
Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
On fast supervised learning for normal mixture models with missing information
Pattern Recognition
Neighborhood Exploring Detector: An EM-Based Signal Detector for Multiple Antenna Systems
IEEE Transactions on Signal Processing
An effective neuro-fuzzy paradigm for machinery condition healthmonitoring
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Visual learning by coevolutionary feature synthesis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Genetic programming for simultaneous feature selection and classifier design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Morphological image enhancement procedure design by using genetic programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
The unconstrained automated generation of cell image features for medical diagnosis
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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A mechanism involving evolutionary genetic programming (GP) and the expectation maximization algorithm (EM) is proposed to generate feature functions automatically, based on the primitive features, for an image pattern recognition system on the diagnosis of the disease OPMD. Prior to the feature function generation, we introduce a novel technique of the primitive texture feature extraction, which deals with non-uniform images, from the histogram region of interest by thresholds (HROIT). Compared with the performance achieved by support vector machine (SVM) using the whole primitive texture features, the GP-EM methodology, as a whole, achieves a better performance of 90.20% recognition rate on diagnosis, while projecting the hyperspace of the primitive features onto the space of a single generated feature.