Useful Links

HMMs

http://ghmm.org/

Python specifics

https://github.com/cmaclell/concept_formation

http://twiecki.github.io/blog/2014/03/17/bayesian-glms-3/

https://danielmescheder.wordpress.com/2011/12/05/training-a-pomdp-with-python/

Papers

Surprisingly very similar (base idea):

http://www.pyoudeyer.com/RAS-SAGG-RIAC-2012.pdf

Different models

http://journal.frontiersin.org/article/10.3389/fncir.2013.00037/full

LVQ

http://www.researchgate.net/profile/Michael_Biehl/publication/224001726_Matrix_learning_in_Learning_Vector_Quantization/links/02bfe5142dc00adbac000000.pdf

Others

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http://link.springer.com/article/10.1007/s10339-011-0404-1

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.142.5238&rep=rep1&type=pdf

http://web.media.mit.edu/~yuanqi/papers/qi-minka-picard-ghahramani-ICML-04.pdf

http://www.degruyter.com/view/j/itit.2014.56.issue-3/itit-2014-1039/itit-2014-1039.xml

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.39.4255&rep=rep1&type=pdf

http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2005_821.pdf

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=4733951

Already in mendeley:

http://papers.nips.cc/paper/3372-a-new-view-of-automatic-relevance-determination.pdf

http://repub.eur.nl/pub/30616/

http://www.mitpressjournals.org/doi/abs/10.1162/neco.2008.10-06-351#.VQGpxsuPUk8

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3869108/

http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00393#.VQGyLMuPUk8

http://link.springer.com/article/10.1023/A:1018245209731

http://webdocs.cs.ualberta.ca/~greiner/C-366/366-SLIDES/dbn-murphy.pdf

http://www.sciencedirect.com/science/article/pii/S1077314206001688

http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0006421

http://www.etsmtl.ca/ETS/media/ImagesETS/Labo/LIVIA/Publications/2012/Khreich_InfoSciences_2012.pdf

http://staffhome.ecm.uwa.edu.au/~00014742/research/projectsbiblio/10.1.1.93.778.pdf

Maybe interesting

http://www.mis.mpg.de/calendar/conferences/2014/al/program.html

http://www.open-ease.org/publications-overview/

http://page.mi.fu-berlin.de/rojas/neural/chapter/K15.pdf

http://cs229.stanford.edu/materials.html

http://www.gaussianprocess.org/gpml/chapters/