Elements of machine learning
Using Generative Models for Handwritten Digit Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
PADO: a new learning architecture for object recognition
Symbolic visual learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Programming and Evolvable Machines
Human-competitive applications of genetic programming
Advances in evolutionary computing
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
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
Evolutionary feature synthesis for facial expression recognition
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
Ptgas---genetic algorithms evolving noncoding segments by means of promoter/terminator sequences
Evolutionary Computation
Artificial Life
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
Visual learning by coevolutionary feature synthesis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multitask visual learning using genetic programming
Evolutionary Computation
Evolving a vision-driven robot controller for real-world indoor navigation
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Genetic Programming and Evolvable Machines
Interest point detection through multiobjective genetic programming
Applied Soft Computing
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This paper introduces a novel method of visual learning based on genetic programming, which evolves a population of individuals (image analysis programs) that process attributed visual primitives derived from raw raster images. The goal is to evolve an image analysis program that correctly recognizes the training concept (shape). The approach uses generative evaluation scheme: individuals are rewarded for reproducing the shape of the object being recognized using graphical primitives and elementary background knowledge encoded in predefined operators. Evolutionary run is driven by a multiobjective fitness function to prevent premature convergence and enable effective exploration of the space of solutions. We present the method in detail and verify it experimentally on the task of learning two visual concepts from examples.