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https://direct.mit.edu/books/book/1821/Learning-with-KernelsSupport-Vector-Machines
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://ieeexplore.ieee.org/book/6267332/
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond Book Abstract: 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 ...
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: 16909
https://www.udemy.com/course/support-vector-machines-in-python/
Support Vector Machines (SVM) are one of the most powerful machine learning models around, and this topic has been one that students have requested ever since I started making courses.. These days, everyone seems to be talking about deep learning, but in fact there was a time when support vector machines were seen as superior to neural networks.One of the things you’ll learn about in this ...4.5/5(285)
https://www.researchgate.net/publication/283925406_Learning_with_kernels_Support_vector_machines_regularization_optimization_and_beyond
Learning with kernels: Support vector machines, regularization, optimization, and beyond Article in IEEE Transactions on Neural Networks 16(3) · January 2005 with 489 Reads How we measure 'reads'Author: Amir Atiya
https://en.wikipedia.org/wiki/Support-vector_machine
The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to the behavior of the hinge loss.
https://jakevdp.github.io/PythonDataScienceHandbook/05.07-support-vector-machines.html
Support vector machines (SVMs) are a particularly powerful and flexible class of supervised algorithms for both classification and regression. In this section, we will develop the intuition behind support vector machines and their use in classification problems. We begin with the standard imports:
https://machinelearningmastery.com/support-vector-machines-for-machine-learning/
Support Vector Machines are perhaps one of the most popular and talked about machine learning algorithms. They were extremely popular around the time they were developed in the 1990s and continue to be the go-to method for a high-performing algorithm with little tuning. In this post you will discover the Support Vector Machine (SVM) machine …
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