What Size Test Set Gives Good Error Rate Estimates?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time classification of traffic signs
Real-Time Imaging
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
The VISTA Project and Its Applications
IEEE Intelligent Systems
Estimating the Generalization Performance of an SVM Efficiently
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Radius margin bounds for support vector machines with the RBF kernel
Neural Computation
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Traffic Sign Classification Using Ring Partitioned Method
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Bounds on Error Expectation for Support Vector Machines
Neural Computation
Circular road signs recognition with soft classifiers
Integrated Computer-Aided Engineering - Artificial Neural Networks
Multilayer perceptrons applied to traffic sign recognition tasks
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Visual sign information extraction and identification by deformable models for intelligent vehicles
IEEE Transactions on Intelligent Transportation Systems
Building Road-Sign Classifiers Using a Trainable Similarity Measure
IEEE Transactions on Intelligent Transportation Systems
Road-Sign Detection and Recognition Based on Support Vector Machines
IEEE Transactions on Intelligent Transportation Systems
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Traffic scene segmentation and robust filtering for road signs recognition
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part I
Symbol recognition in natural scenes by shape matching across multi-scale segmentations
GREC'11 Proceedings of the 9th international conference on Graphics Recognition: new trends and challenges
Journal of Visual Communication and Image Representation
Traffic sign recognition using group sparse coding
Information Sciences: an International Journal
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Pattern recognition methods are used in the final stage of a traffic sign detection and recognition system, where the main objective is to categorize a detected sign. Support vector machines have been reported as a good method to achieve this main target due to their ability to provide good accuracy as well as being sparse methods. Nevertheless, for complete data sets of traffic signs the number of operations needed in the test phase is still large, whereas the accuracy needs to be improved. The objectives of this work are to propose pre-processing methods and improvements in support vector machines to increase the accuracy achieved while the number of support vectors, and thus the number of operations needed in the test phase, is reduced. Results show that with the proposed methods the accuracy is increased 3-5% with a reduction in the number of support vectors of 50-70%.