Open AccessProceedings Article
Support Vector Regression Machines
Harris Drucker,Christopher John Burges,Linda Kaufman,Alexander J. Smola,Vladimir Vapnik +4 more
- Vol. 9, pp 155-161
Reads0
Chats0
TLDR
This work compares support vector regression (SVR) with a committee regression technique (bagging) based on regression trees and ridge regression done in feature space and expects that SVR will have advantages in high dimensionality space because SVR optimization does not depend on the dimensionality of the input space.Abstract:
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.read more
Citations
More filters
Book
Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
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.
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.
Journal ArticleDOI
Robust enumeration of cell subsets from tissue expression profiles
Aaron M. Newman,Chih Long Liu,Michael R. Green,Andrew J. Gentles,Weiguo Feng,Yue Xu,Chuong D. Hoang,Maximilian Diehn,Arash Ash Alizadeh +8 more
TL;DR: CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types when applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors.
Proceedings ArticleDOI
Advances in kernel methods: support vector learning
TL;DR: Support vector machines for dynamic reconstruction of a chaotic system, Klaus-Robert Muller et al pairwise classification and support vector machines, Ulrich Kressel.
References
More filters
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
Bagging predictors
TL;DR: Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy.
Journal ArticleDOI
Multivariate Adaptive Regression Splines
TL;DR: In this article, a new method is presented for flexible regression modeling of high dimensional data, which takes the form of an expansion in product spline basis functions, where the number of basis functions as well as the parameters associated with each one (product degree and knot locations) are automatically determined by the data.
Journal ArticleDOI
The Nature Of Statistical Learning Theory
TL;DR: As one of the part of book categories, the nature of statistical learning theory always becomes the most wanted book.