Feature Selection Package - Algorithms - Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation (SBMLR)
Description
SBMLR is a method for recognizing multi-class patterns.
Usage
Method Signature:
[out] = fsSBMLR(X, Y)
Output:
out:
A struct containing the following fields:
- w - a list containing the information gain of each feature
when matched with fList.
- fList - the list of features ranked by their ability to classify
the data.
Input:
X:
The features on current trunk, each column is a feature vector on all
instances, and each row is a part of the instance.
Y:
The label of instances, in single column form: 1 2 3 4 5 ...
Code Example
% Using the wine.dat data set, which can be found at
% [fspackage_location]/classifiers/knn/wine.mat
fsSBMLR(X,Y);
Keyword in Evaluator Framework
sbmlr
Paper
BibTex entry for:
Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation
@inproceedings{DBLP:conf/nips/CawleyTG06,
author = {Gavin C. Cawley and Nicola L. C. Talbot and Mark Girolami},
title = {Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation},
booktitle = {NIPS},
year = {2006},
pages = {209-216},
ee = {http://books.nips.cc/papers/files/nips19/NIPS2006_0216.pdf},
crossref = {DBLP:conf/nips/2006},
bibsource = {DBLP, http://dblp.uni-trier.de}
}