I know something needs to be configured in CrossValidation, SMO or MSVM (I have been trying with several parameters for the Gaussian and Polynomial kernels, and the complexity parameter as well), but I have looked at the documentation and I don't know what to change. At the moment I can compute the SIFT feature vectors for an image, and have implemented a SVM, however am finding it hard to understand the literature on how use the bag of words model to 'vector quantize' the SIFT features and build histograms that give fixed size vectors, that can be used to train and test the SVM. Jan 14, 2017 · Creating neural networks for pattern recognition is straightforward. You just have to define the training algorithm, hidden layer size and type of network (patternnet, here). In order to assess the performance of the pattern recognition network, you have to define test and validation sets and a performance metric.
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