Localized Support Vector Regression

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Localized support vector regression for time series ...

    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

Localized support vector regression for time series ...

    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

Localized support vector regression for time series ...

    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'

Support Vector Machine - Regression (SVR)

    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 ...

LSRR-LA: An Anisotropy-Tolerant Localization Algorithm ...

    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

An Adaptive Support Vector Regression Machine for the ...

    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

Understanding Support Vector Machine Regression - MATLAB ...

    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.

Linear Regression and Support Vector Regression

    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

Quantitative Robustness of Localized Support Vector Machines

    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

Support-vector machine - Wikipedia

    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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