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https://www.amazon.com/Learning-Kernels-Regularization-Optimization-Computation/dp/0262194759
Mar 02, 2018 · Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning) [Bernhard Schlkopf, Alexander J. Smola] on Amazon.com. *FREE* shipping on qualifying offers. A comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990sCited by: 16910
https://direct.mit.edu/books/book/1821/Learning-with-KernelsSupport-Vector-Machines
A comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM).
https://www.amazon.com/Learning-Kernels-Regularization-Optimization-Computation-ebook/dp/B00ELWF69I
Mar 02, 2018 · Interesting and original. Learning with Kernels will make a fine textbook on this subject. (Grace Wahba, Bascom Professor of Statistics, University of Wisconsin Madison) This splendid book fills the need for a comprehensive treatment of kernel methods and support vector machines.4.5/5(15)
https://ieeexplore.ieee.org/book/6267332/
In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs -- -kernels--for a number of learning tasks ...
http://www.cs.cas.cz/~petra/slides/svm.pdf
Introduction Binary classification Learning with Kernels Support Vector Machines Demo Conclusion Learning from data find a general rule that explains data given only as a sample of limited size data may contain measurement errors or noise supervised learning data are sample of input-output pairs find input-output mapping
https://www.abebooks.com/9780262194754/Learning-Kernels-Support-Vector-Machines-0262194759/plp
AbeBooks.com: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning) (9780262194754) by Schlkopf, Bernhard; Smola, Alexander J. and a great selection of similar New, …4.1/5(32)
https://dl.acm.org/doi/book/10.5555/559923
In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs -kernels--for a number of learning tasks.
https://www.quora.com/What-are-kernels-in-machine-learning-and-SVM-and-why-do-we-need-them
It is not possible to find a hyperplane or a linear decision boundary for some classification problems. If we project the data in to a higher dimension from the original space, we may get a hyperplane in the projected dimension that helps to class...
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