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https://www.sciencedirect.com/science/article/abs/pii/S0925231208004529
One problem of the standard SVR is that it considers data in a global fashion only. Therefore it may lack the flexibility to capture the local trend of data; this is a critical aspect of volatile data, especially financial time series data. Aiming to attack this issue, we propose the localized support vector regression …Cited by: 153
https://www.sciencedirect.com/science/article/pii/S0925231208004529
In this paper, we have proposed the localized support vector regression (LSVR) model in order to improve the performance of the standard support vector regression (SVR) model for time series prediction. In contrast to the standard SVR model, our novel model offers a systematic and automatic scheme to adapt the margin locally and flexibly.Cited by: 153
https://www.researchgate.net/publication/222076085_Localized_support_vector_regression_for_time_series_prediction
Localized support vector regression for time series prediction Article in Neurocomputing 72(10-12):2659-2669 · June 2009 with 33 Reads How we measure 'reads'
https://www.saedsayad.com/support_vector_machine_reg.htm
The Support Vector Regression (SVR) uses the same principles as the SVM for classification, with only a few minor differences. First of all, because output is a real number it becomes very difficult to predict the information at hand, which has infinite possibilities. In the case of regression, a margin of tolerance (epsilon) is set in ...
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6263435/
Inspired by the PDM method in , Lee et al. presented two improved localization methods, i.e., Localized Support Vector Regression (LSVR) and Multi-dimensional Support Vector Regression (MSVR) , which regard the relationship between hop counts and distances as nonlinear and estimate distances by the regression method relying on a Support Vector ...Cited by: 1
https://www.hindawi.com/journals/sv/2015/469165/
To improve the adaptability of volatile time series, several modified SVR machines, such as localized support vector regression (LSVR) and piecewise support vector regression , have been developed and applied in the field of financial analysis. In this paper, a novel SVR machine, called adaptive support vector regression (ASVR), is proposed to ...Cited by: 2
https://www.mathworks.com/help/stats/understanding-support-vector-machine-regression.html
Understanding Support Vector Machine Regression Mathematical Formulation of SVM Regression Overview. Support vector machine (SVM) analysis is a popular machine learning tool for classification and regression, first identified by Vladimir Vapnik and his colleagues in 1992.SVM regression is considered a nonparametric technique because it relies on kernel functions.
https://cs.adelaide.edu.au/~chhshen/teaching/ML_SVR.pdf
Regression Overview CLUSTERING CLASSIFICATION REGRESSION (THIS TALK) K-means •Decision tree •Linear Discriminant Analysis •Neural Networks •Support Vector Machines •Boosting •Linear Regression •Support Vector Regression Group data based on their characteristics Separate data based on their labels Find a model that can explain
https://arxiv.org/pdf/1903.01334
Quantitative Robustness of Localized Support Vector Machines Florian Dumpert Department of Mathematics, University of Bayreuth, Germany Abstract The huge amount of available data nowadays is a challenge for kernel-based machine learning algorithms like SVMs with respect to runtime and storage capacities. Local approaches mightAuthor: Florian Dumpert
https://en.wikipedia.org/wiki/Support-vector_machine
In machine learning, support-vector machines (SVMs, also support-vector networks) 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 ...
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