• Corpus ID: 743542

Support Vector Regression Machines

@inproceedings{Drucker1996SupportVR,
  title={Support Vector Regression Machines},
  author={Harris Drucker and Christopher J. C. Burges and Linda Kaufman and Alex Smola and Vladimir Naumovich Vapnik},
  booktitle={NIPS},
  year={1996}
}
A new regression technique based on Vapnik's concept of support vectors is introduced. We compare support vector regression (SVR) with a committee regression technique (bagging) based on regression trees and ridge regression done in feature space. On the basis of these experiments, it is expected that SVR will have advantages in high dimensionality space because SVR optimization does not depend on the dimensionality of the input space. 

Figures and Tables from this paper

Stochastic support vector regression with probabilistic constraints

A novel model of SVR is introduced in which any training samples containing inputs and outputs are considered the random variables with known or unknown distribution functions which helps to obtain maximum margin and achieve robustness.

Support vector fuzzy regression machines

Interval Regression Analysis with Reduced Support Vector Machine

This paper introduces the principle of RSVM to evaluate interval regression analysis and shows that it has been proved more efficient than the traditional SVM in processing large-scaled data.

Balanced Support Vector Regression

  • M. Orchel
  • Computer Science, Mathematics
    ICAISC
  • 2015
A method to incorporate the idea of regression to support vector regression (SVR) by adding an equality constraint to the SVR optimization problem with improved generalization performance for suboptimal values of e and δ.

Support vector machines: hype or hallelujah?

An intuitive explanation of SVMs from a geometric perspective is provided and the classification problem is used to investigate the basic concepts behind SVMs and to examine their strengths and weaknesses from a data mining perspective.

Support Vector Regression

An attempt has been made to review the existing theory, methods, recent developments and scopes of Support Vector Regression.

Bootstrap Based Pattern Selection for Support Vector Regression

A pattern selection method designed specifically for Support Vector Regression (SVR), where only a few patterns called support vectors are used to construct the regression model while other patterns are not used at all.

Kernel Support Vector Regression with imprecise output

We consider a regression problem where uncertainty aects to the dependent variable of the elements of the database. A model based on the standard -Support Vector Regression approach is given, where

Feature selection for support vector regression via Kernel penalization

  • S. MaldonadoR. Weber
  • Computer Science
    The 2010 International Joint Conference on Neural Networks (IJCNN)
  • 2010
This paper presents a novel feature selection approach (KP-SVR) that determines a non-linear regression function with minimal error and simultaneously minimizes the number of features by penalizing
...

References

SHOWING 1-8 OF 8 REFERENCES

Subset Selection in Regression

8. Subset Selection in Regression (Monographs on Statistics and Applied Probability, no. 40). By A. J. Miller. ISBN 0 412 35380 6. Chapman and Hall, London, 1990. 240 pp. £25.00.

Subset Selection in Regression.

OBJECTIVES Prediction, Explanation, Elimination or What? How Many Variables in the Prediction Formula? Alternatives to Using Subsets 'Black Box' Use of Best-Subsets Techniques LEAST-SQUARES

A computational method for the indefinite quadratic programming problem

The Nature Of Statistical Learning Theory

As one of the part of book categories, the nature of statistical learning theory always becomes the most wanted book.

Introduction to applied mathematics

Introduction to applied mathematics , Introduction to applied mathematics , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی

The Nature of Statistical Learning

Bagging Predictors CA Also at anonymous ftp site: ftp

  • Bagging Predictors CA Also at anonymous ftp site: ftp
  • 1994