Hey guys,
>From a very high level point of view of someone who is having a hard time
reviewing the pull requests, but still tries to have a big-picture view
of the project, you have a very strong support on the idea of
implementing and benching in good examples those standard
outlier-detection alg
Hi Nicholas,
I have some works on outlier detection, let me know how can I help, once
you start this project.
Thanks,
via Mobile.
Dayvid V. R. Oliveira
PhD candidate in Computer Science - UFPE
MSc in Computer Science - UFPE
Computer Engineer - UFPE
On Jun 23, 2014 12:52 PM, "Nicolas Goix"
Hello,
The following study evaluates on the DARPA 1998 data set four outlier
detection algorithms :
Unserpervised SVM, LOF approach, NN approach and Mahalanobis-based approach
:
http://static.msi.umn.edu/rreports/2003/72.pdf
They find the LOF approach to be the more efficient, followed by the
hi,
Nicolas, could you give some numbers on the impact of these different works
to get an idea of which work might have the highest interest for the
sklearn community? do they all scale to medium or large datasets?
is there anybody on the list with experience with these tools?
Best,
Alex
On Fr
Hello,
This is my first post to the list, I have been recently in touch with
Alexandre Gramfort, and I would be very interested in exploring some
outlier/anomaly detection algorithms, before eventually put it in a
compatible scikit learn API (with a view to eventually merge it).
I'm not particula
