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http://web.stanford.edu/group/mmds/slides2008/malik.pdf
Classification Using Intersection Kernel Support Vector Machines is efficient. Subhransu Maji and Alexander C. Berg and Jitendra Malik. Proceedings of CVPR 2008, Anchorage, Alaska, June 2008.
https://people.cs.umass.edu/~smaji/papers/iksvm-cvpr08.pdf
Classification using Intersection Kernel Support Vector Machines is Efficient ... with the histogram intersection kernel, and generalizations ... We first begin with a review of support vector machines for classification. Given labeled training data of the form {(yi,xi)}N
https://www.sciencedirect.com/science/article/pii/S0925231218312207
A kernel support vector machine (SVM) is one of the most popular classifiers, and it has been applied to a variety of fields due to its excellent classification performance , , , , , , , . Typical examples of nonlinear kernels include the polynomial kernel, the radial basis function (RBF) kernel and the Gaussian kernel, which definitely outperform linear SVM.Author: Jeonghyun Baek, Euntai Kim
https://ieeexplore.ieee.org/abstract/document/4587630/
Jun 28, 2008 · Classification using intersection kernel support vector machines is efficient Abstract: Straightforward classification using kernelized SVMs requires evaluating the kernel for a test vector and each of the support vectors. For a class of kernels we show that one can do this much more efficiently.Cited by: 1199
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.3974
For a class of kernels we show that one can do this much more efficiently. In particular we show that one can build histogram intersection kernel SVMs (IKSVMs) with runtime complexity of the classifier logarithmic in the number of support vectors as opposed to linear for the standard approach.
https://www.sciencedirect.com/science/article/pii/S104732031630178X
CCM intersection kernel support vector machines (CIKSVMs) The feasibility and advantage of generalizing HIKSVMs in our approach is based on following facts. First, a CCM can be identified as a histogram from the view of isomorphism of probability distributions.Author: Jian Zou, Gui-Fu Lu, Yue Zhang, Chuancai Liu
https://heartbeat.fritz.ai/understanding-the-mathematics-behind-support-vector-machines-5e20243d64d5
In this post, we’re going to unravel the mathematics behind a very famous, robust, and versatile machine learning algorithm: support vector machines. We’ll also gain insight on relevant terms like kernel tricks, support vectors, cost functions for SVM, etc.
https://www.holehouse.org/mlclass/12_Support_Vector_Machines.html
One final supervised learning algorithm that is widely used - support vector machine (SVM) Compared to both logistic regression and neural networks, a SVM sometimes gives a cleaner way of learning non-linear functions; ... The distance between the intersection and the origin is ...
http://web.mit.edu/6.034/wwwbob/svm.pdf
•Support vector machines Support Vectors again for linearly separable case •Support vectors are the elements of the training set that would change the position of the dividing hyperplane if removed. •Support vectors are the critical elements of the training set …
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