.png){kind=icon}
Return
A tutorial on support vector machine-based methods for classification problems in chemometrics
.png){kind=icon}
PRE
AI
DOI：10.1016/j.aca.2010.03.030.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This tutorial provides a concise overview of support vector machines and different closely related techniques for pattern classification. The tutorial starts with the formulation of support vector machines for classification. The method of least squares support vector machines is explained. Approaches to retrieve a probabilistic interpretation are covered and it is explained how the binary classification techniques can be extended to multi-class methods. Kernel logistic regression, which is closely related to iteratively weighted least squares support vector machines, is discussed. Different practical aspects of these methods are addressed: the issue of feature selection, parameter tuning, unbalanced data sets, model evaluation and statistical comparison. The different concepts are illustrated on three real-life applications in the field of metabolomics, genetics and proteomics. (C) 2010 Elsevier B.V. All rights reserved.
Keywords:
Support vector machine
Least squares support vector machine
Kernel logistic regression
Kernel-based learning
Feature selection
Multi-class probabilities
Journal
IF:
6
Papers:
3.3W
Citations:
6.1W
Organization
Cited Papers
Biochemistry of long-chain, nonisoprenoid hydrocarbons. I. Characterization of the hydrocarbons of Sarcina lutea and the isolation of possible intermediates of biosynthesis.png)
Biochemistry
BiochemistryIF0
PREAI
ShareSave
ShareSave
THE MEANING AND USE OF THE AREA UNDER A RECEIVER OPERATING CHARACTERISTIC (ROC) CURVERADIOLOGY
RADIOLOGYIF15.2
PREAI
ShareSaveShareSaveShareSaveShareSaveShareSave
View more