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https://www.codeproject.com/articles/106583/handwriting-recognition-revisited-kernel-support-v
Sep 01, 2010 · In a previous article, we discussed how to perform the recognition of handwritten digits using Kernel Discriminant Analysis. In this article, we will discuss some techniques to do it using Kernel Support Vector Machines.4.9/5(96)
https://ieeexplore.ieee.org/document/1030883/
Aug 08, 2002 · Online handwriting recognition with support vector machines - a kernel approach Abstract: In this paper we describe a novel classification approach for online handwriting recognition. The technique combines dynamic time warping (DTW) and support vector machines (SVMs) by establishing a new SVM kernel. We call this kernel Gaussian DTW (GDTW) kernel.
https://arxiv.org/abs/1203.3847
Then the SVM is used to estimate global correlations and classify the pattern. Support Vector Machine (SVM) is an alternative to NN. In Handwritten recognition, SVM gives a better recognition result. The aim of this paper is to develop an approach which improve the efficiency of handwritten recognition using artificial neural network
https://arxiv.org/pdf/1203.3847
Support Vector Machine (SVM) is an alternative to NN. In Handwritten recognition, SVM gives a better recognition result. The aim of this paper is to develop an approach which improve the efficiency of handwritten recognition using artificial neural network Keyword: Handwriting recognition, Support Vector Machine, Neural Network 1. IntroductionCited by: 4
https://www.academia.edu/27857545/Online_handwriting_recognition_using_support_vector_machine
The [8] C. Bahlmann, B. Haasdonk, H. Burkhardt., “Online number of support vectors can be reduced by selecting Handwriting Recognition with Support Vector better C and gamma parameter values through a finer Machine – A Kernel Approach”, In proceeding of grid search and by reduced set selection [5][6].
http://www.pybloggers.com/2016/02/using-support-vector-machines-for-digit-recognition/
The solution to this is to train multiple Support Vector Machines, that solve problems stated in this format: “Is this digit a 3 or not a 3?”. Now we are solving a binary classification again with the two classes “is a 3” and “is not a 3”. In our case we have one Support Vector Machine for each digit, giving us a total of ten.
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