Changes sfa-tk.V2.8. 1

Changes sfa-tk.V2.7. 1

Changes sfa-tk.V2.6. 2

Literature. 3

Changes sfa-tk.V2.8

Author:            Wolfgang Konen, wolfgang.konen@fh-koeln.de

Created:          Nov’2011

Last modified: Nov’2011

 

This chapter is a summary of changes in sfa-tk.V2.8 as compared to sfa-tk.V2.7:

If opts.CL is missing, sfa_classify.m will generate a default object
All parameters related to the classifiers (Gauss or Nearest Neighbour) are now concentrated in struct opts.CL:

 

The new things in this version are: automatic parametric bootstrap, regularization of Gauss classifier and optional Nearest Neighbour classifier. This all should make the default SFA classification algorithm fairly robust for marginal training data.

 

For a description of demo files and demo datasets now included in the distribution see demo\AAREADME.htm.

Changes sfa-tk.V2.7

Author:            Wolfgang Konen, wolfgang.konen@fh-koeln.de

Created:          Jan’2011

Last modified: Jan’2011

 

This chapter is a summary of changes in sfa-tk.V2.7 as compared to sfa-tk.V2.6:

 

The new thing is here that sfaClassModel.m can store everything relevant for the classifier model (e.g. a model built from training data), so that at a later point in time new test data can be classified without access to the training data and without a need to run SFA again.

 

Scripts and other files for experiments have been moved to sfa-tk/experim (may not be included in distribution)

 

For a description of demo files and demo datasets now included in the distribution see demo\AAREADME.htm.

Changes sfa-tk.V2.6

Author V2.6:   Wolfgang Konen, wolfgang.konen@fh-koeln.de

Created:          Nov’2009

Last modified: Nov’2009

(Author V1.0:  Pietro Berkes, berkes@brandeis.edu)

 

For an in-depth description of the new algorithm SVD_SFA see [Konen09b].

 

This chapter is a summary of changes in sfa-tk.V2.6 as compared to sfa-tk.V1.0.1 [Berkes03]:

Literature

·        [Koch10a] Koch, P., Konen, W., Hein, K., Gesture Recognition on Few Training Data using Slow Feature Analysis and Parametric Bootstrap. In P. Sobrevilla (ed.), Proc. IEEE World Congress on Computational Intelligence (WCCI), Barcelona, July 2010. (PDF)

·        [Konen11a] Konen, W. (2011). Der SFA-Algorithmus für Klassifikation. CIOP Technical Report 08/2011, Cologne University of Applied Sciences. (PDF)

·        [Konen11b] Konen, W. (2011). SFA classification with few training data: Improvements with parametric bootstrap. CIOP Technical Report 09/2011, Cologne University of Applied Sciences. (PDF)