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https://www.sciencedirect.com/science/article/pii/S0925231201006762
Modified support vector machines in financial time series forecasting The financial market is a complex, evolutionary, and nonlinear dynamical system. The financial time series are inherently noisy, non-stationary, and deterministically chaotic. This means that the distribution of financial time series is changing over the time.Cited by: 525
https://www.sciencedirect.com/science/article/abs/pii/S0925231201006762
This paper proposes a modified version of support vector machines, called C-ascending support vector machine, to model non-stationary financial time series. The C -ascending support vector machines are obtained by a simple modification of the regularized risk function in support vector machines, whereby the recent ε -insensitive errors are penalized more heavily than the distant ε …Cited by: 525
https://en.wikipedia.org/wiki/Support-vector_machine
In machine learning, support-vector machines are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier. An SVM model …
http://people.uncw.edu/chenc/STT450/PPT/Chapter%2009_Support%20Vector%20Machines.pptx
Support Vector Machines (SVM) Chapter 09. Disclaimer: This PPT is modified based on IOM 530: Intro. to Statistical Learning. STT592-002: Intro. to Statistical Learning . 9.1 Support Vector Classifier. Classification with a separating Hyperplanes. STT592-002: Intro. to Statistical Learning .
https://machinelearningmastery.com/support-vector-machines-for-machine-learning/
Support Vector Machines (Kernels) The SVM algorithm is implemented in practice using a kernel. The learning of the hyperplane in linear SVM is done by transforming the problem using some linear algebra, which is out of the scope of this introduction to SVM.
https://www.researchgate.net/publication/331179438_Support_Vector_Machines
This kernel is used to build an embedding of data in a variety that will allow the use of a (modified) one-class Support Vector Machine to detect outliers. We study several information combination ...
https://www.quora.com/What-are-some-pros-and-cons-of-Support-Vector-Machines
Sep 12, 2019 · The two main advantages of support vector machines are that: 1. They’re accurate in high dimensional spaces; 2. and, they use a subset of training points in the decision function (called support vectors), so it’s also memory efficient. The disadva...
https://stats.stackexchange.com/questions/73032/linear-kernel-and-non-linear-kernel-for-support-vector-machine
Linear kernel and non-linear kernel for support vector machine? Ask Question Asked 6 years, 3 months ago. Active 1 year ago. Viewed 64k times 46. 27 $\begingroup$ When using support vector machine, are there any guidelines on choosing linear kernel vs. nonlinear kernel, like RBF? I once heard that non-linear kernel tends not to perform well ...
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