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Support vector machine (SVM) is an optimal margin based classification technique in machine learning. SVM is a binary linear classifier which has been extended to non-linear data using Kernels and multi-class data using various techniques like one-versus-one, one-versus-rest, Crammer Singer SVM, Weston Watkins SVM and directed acyclic graph SVM (DAGSVM) etc. SVM with a linear Kernel is called linear...
The computation of features is an integral part of handwriting recognition as identification of correct features is essential for efficient data representation and benchmarked recognition. In context of handwriting recognition, the aim of feature extraction is to find out the certain properties of a handwritten stroke that best describe the class of a stroke and makes it distinguishable from other...
A simple, distributed and sequential procedure has been implemented that include computation of support vectors and use of support vectors data as input to neural networks training. This way we are able to reduce training data to one third and one sixth of original data set after using support vector machines training with radial basis function and polynomial kernels, respectively. The two times training...
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