Using Generative Models for Handwritten Digit Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning - Special issue on inductive transfer
PADO: a new learning architecture for object recognition
Symbolic visual learning
ICES '96 Proceedings of the First International Conference on Evolvable Systems: From Biology to Hardware
Evolving Modules in Genetic Programming by Subtree Encapsulation
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Improved Rooftop Detection in Aerial Images with Machine Learning
Machine Learning
Generative Models and Bayesian Model Comparison for Shape Recognition
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Evolutionary Synthesis of Pattern Recognition Systems (Monographs in Computer Science)
Evolutionary Synthesis of Pattern Recognition Systems (Monographs in Computer Science)
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
Genetic programming for cross-task knowledge sharing
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Learning and Recognition of Hand-Drawn Shapes Using Generative Genetic Programming
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Learning high-level visual concepts using attributed primitives and genetic programming
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Evolving pattern recognition systems
IEEE Transactions on Evolutionary Computation
Learning with case-injected genetic algorithms
IEEE Transactions on Evolutionary Computation
Visual learning by coevolutionary feature synthesis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Genetic programming for cross-task knowledge sharing
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Multi-task code reuse in genetic programming
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Multitask visual learning using genetic programming
Evolutionary Computation
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We propose a method of knowledge reuse for an ensemble of genetic programming-based learners solving a visual learning task. First, we introduce a visual learning method that uses genetic programming individuals to represent hypotheses. Individuals-hypotheses process image representation composed of visual primitives derived from the training images that contain objects to be recognized. The process of recognition is generative, i.e., an individual is supposed to restore the shape of the processed object by drawing its reproduction on a separate canvas. This canonical method is extended with a knowledge reuse mechanism that allows a learner to import genetic material from hypotheses that evolved for the other decision classes (object classes). We compare the performance of the extended approach to the basic method on a real-world tasks of handwritten character recognition, and conclude that knowledge reuse leads to significant convergence speedup and, more importantly, significantly reduces the risk of overfitting.