classyfire-package: Robust multivariate classification using highly optimised SVM...

In classyfire: Robust multivariate classification using highly optimised SVM ensembles

Description

The aim of the classyfire package is to improve the quality of multivariate classification projects by making a state-of-the-art multivariate classification workflow available to everyone. Classyfire achieves this by providing powerful functions which automate as much of the classifier building and testing as possible. However, to avoid these functions becoming impenetrable black boxes, detailed information is provided about how these functions work, and full access is provided to the internals of all classifiers that are produced.

Details

Package:	classyfire
Type:	Package
Version:	0.1-2
Date:	2015-01-11
License:	GPL (>= 2)

Author(s)

Adapted functionality by Eleni Chatzimichali ([e-mail])

Author of the SVM functions: David Meyer (<[e-mail]>)
(based on C/C++-code by Chih-Chung Chang and Chih-Jen Lin)
Author of Scilab neldermead module: Michael Baudin (INRIA - Digiteo)
Author of Scilab R adaptation: Sebastien Bihorel (<[e-mail]>)
Authors of bootstrap functions: Angelo Canty and Brian Ripley (originally by Angelo Canty for S)

References

There are many references explaining the concepts behind the functionality of this package. Among them are :

Chang, Chih-Chung and Lin, Chih-Jen:
LIBSVM: a library for Support Vector Machines
http://www.csie.ntu.edu.tw/~cjlin/libsvm

Exact formulations of models, algorithms, etc. can be found in the document:
Chang, Chih-Chung and Lin, Chih-Jen:
LIBSVM: a library for Support Vector Machines
http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.ps.gz

More implementation details and speed benchmarks can be found on:
Rong-En Fan and Pai-Hsune Chen and Chih-Jen Lin:
Working Set Selection Using the Second Order Information for Training SVM
http://www.csie.ntu.edu.tw/~cjlin/papers/quadworkset.pdf

Spendley, W. and Hext, G. R. and Himsworth, F. R.
Sequential Application of Simplex Designs in Optimisation and Evolutionary Operation
American Statistical Association and American Society for Quality, 1962

Nelder, J. A. and Mead, R.
A Simplex Method for Function Minimization
The Computer Journal, 1965

C. T. Kelley
Iterative Methods for Optimization
SIAM Frontiers in Applied Mathematics, 1999

A. C. Davison and D. V. Hinkley
Bootstrap Methods and Their Applications
CUP, 1997

Booth, J.G., Hall, P. and Wood, A.T.A.
Balanced importance resampling for the bootstrap.
Annals of Statistics, 21, 286-298, 1993

Davison, A.C. and Hinkley, D.V.
Bootstrap Methods and Their Application
Cambridge University Press, 1997

Efron, B. and Tibshirani, R.
An Introduction to the Bootstrap
Chapman & Hall, 1993

classyfire documentation built on May 29, 2017, 11:05 p.m.