Journal ArticleDOI
Support vector regression for link load prediction
Paola Bermolen,Dario Rossi +1 more
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TLDR
This work is the first to explore the use of support vector machines (SVM) for the purpose of link load forecast, and finds that while SVM robustness is more than satisfactory, accuracy results are just close to be tempting, but not enough to convince.About:
This article is published in Computer Networks.The article was published on 2009-02-01. It has received 87 citations till now. The article focuses on the topics: Supervised learning.read more
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Journal ArticleDOI
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities
Raouf Boutaba,Mohammad A. Salahuddin,Noura Limam,Sara Ayoubi,Nashid Shahriar,Felipe Estrada-Solano,Felipe Estrada-Solano,Oscar Mauricio Caicedo +7 more
TL;DR: This survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking, and jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies.
Journal ArticleDOI
Short-term electricity prices forecasting based on support vector regression and Auto-regressive integrated moving average modeling
Jinxing Che,Jianzhou Wang +1 more
TL;DR: The experimental results demonstrate that the model proposed outperforms the existing neural-network approaches, the traditional ARIMA models and other hybrid models based on the root mean square error and mean absolute percentage error.
Journal ArticleDOI
Elastic Business Process Management
TL;DR: This paper conceptualizes an architecture for an elastic Business Process Management System and discusses existing work on scheduling, resource allocation, monitoring, decentralized coordination, and state management for elastic processes.
Journal ArticleDOI
Support vector regression methodology for wind turbine reaction torque prediction with power-split hydrostatic continuous variable transmission
Shahaboddin Shamshirband,Dalibor Petković,Amineh Amini,Nor Badrul Anuar,Vlastimir Nikolić,Žarko Ćojbašić,Miss Laiha Mat Kiah,Abdullah Gani +7 more
TL;DR: In this paper, the polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) for prediction of wind turbine reaction torque.
Journal ArticleDOI
Machine Learning Regression Techniques for the Silage Maize Yield Prediction Using Time-Series Images of Landsat 8 OLI
TL;DR: This research demonstrated that some advanced ML approaches can predict the silage maize yield and they are less sensitive to inconsistency of NDVI time series.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI
A Tutorial on Support Vector Machines for Pattern Recognition
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Book
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
TL;DR: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory, and will guide practitioners to updated literature, new applications, and on-line software.
Proceedings ArticleDOI
A training algorithm for optimal margin classifiers
TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.
Journal ArticleDOI
A tutorial on support vector regression
TL;DR: This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.