Computational Intelligence in Traffic Sign Recognition
Figure 14 Separation in a higher dimension
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Figure 14 Separation in a higher dimension. The SVM algorithm consists of two stages: Training stage: training samples containing labelled positive and negative input data to the SVM. This input data can consist of distance to border vectors, binary images, Zernike moments, and more. Each input data is represented by vector  with label  , l is the number of samples. The decision boundary should classify all points correctly, thus  . The decision boundary can be found by solving the following constrained optimization problem:  . The Lagrangian of this optimization problem is:  . The optimization problem can be rewritten in terms of  by setting the derivative of the Lagrangian to zero:  This quadratic programming problem is solver when:  with  are support vectors. This is for a linear separable problem, for more details about the non-linear problem see Appendix .
Testing stage: the resulting classifier is applied to unlabeled images to decide whether they belong to the positive or the negative category. The label of  is simply obtained by computing:  with  the indices of the s support vectors. Classify Directory: Images Images -> Pelan pentaksiran kursus Images -> Military callsign list as of dec 2010 Images -> For Date: 02/01/2013 Friday Call Number Time Call Reason Action 13-768 0008 motor vehicle stop warning Issued Images -> New embedded S Images -> Creating Buy-In – Building Relationships Images -> Deadline: Saturday, March 12th, 2016 Images -> News items from Raising Our Voices Images -> Last year four of the best Afrikaans Christian singers joined forces to tour Down Under with the production ‘Manne wat Glo’ Images -> World-renowned vocal ensemble sweet honey in the rock Images -> Peoples Voice Café History Download 2.08 Mb. Share with your friends:
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