According to Bolle et al. [1] it is possible to distinguish three different fusion levels:

1) Data-Level Fusion (use of multiple sensors or mutliple traits to acquire)

2) Feature-Level Fusion (use of multiple features)

3) Decision-Level Fusion (use of multiple classifieres)

[1] Ruud M. Bolle et al.: "Guide to Biometrics", ISBN: 0-387-40089-3, Springer New York, 2003

What you are looking for sounds pretty much like a decision-level fusion. You have 'n' classifiers resulting in 'n' decisions which need to be combined.

A very simple example for an equally weighted decision-level fusion using two different classifieres would be a simple logical combination. For simplicity we stick to a 2-class classification scenario where the classification result is either 'true' or 'false'.

For example we could simply combine these two decisions using logical AND or OR operator: 'decision classifier 1' AND 'decision classifier 2' = 'fused decision'.

I hope that helps.

Hi,

Not sure about SVM but i remember using RIPPER (J-RIP, WEKA) and getting pretty decent result on some vision stuff, a while back. You may want to check out the paper to see if it is relevant to your interests:

@inproceedings{

Cohen1995, author = {William W. Cohen},

booktitle = {Twelfth International Conference on Machine Learning},

pages = {115-123}, publisher = {Morgan Kaufmann},

title = {Fast Effective Rule Induction},

year = {1995} }

Java code:

http://weka.sourceforge.net/doc.stable/weka/classifiers/rules/JRip.html

Looks like Harry potter used it recently..... ;)

http://scholar.google.co.uk/scholar?as_ylo=2013&hl=en&as_sdt=0,5&sciodt=0,5&cites=17978342459423459742&scipsc=