

Visualisation of Machine Learning Algorithms - maurits
http://mldemos.epfl.ch/

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vedantk
The install.sh and install_script.sh scripts are Mac-dependent. How would you
compile this on Linux? Qmake?

edit: Yep. Clone the git repo, then run qmake, followed by make.

edit #2: Here is a patch I wrote to make the project compile under OpenCV 2.2,
and Linux: <https://gist.github.com/999061>

~~~
microsage
Even after applying your patch I had a little trouble getting everything to
link properly on x86_64 Fedora (14). I ended up manually tweaking my make
files after running qmake.

Changed the LIBS line (in MLDemos/Makefile,
_AlgorithmPlugins/<n>/Makefile.<n>, _IOPlugins/<n>/Makefile.<n>) to:

LIBS = $(SUBLIBS) -L/usr/lib64 -L/usr/local/lib -lhighgui -lcv -lcxcore
-lQtGui -lQtCore -lpthread

I suspect there's an easier way, but this at least allowed me to build and
everything seems to work properly.

~~~
vedantk
I'm glad it built.

I think the issue we're all having stems from the structural redesign of
opencv 2.2. Everything has been split up into smaller headers, and the
functionality is similarly separated into different shared libraries. It's a
more logical code structure, but it means that we need to have different
makefile rules for pre-2.2 and 2.2+ code.

As far as -lhighgui -lcv -lcxcore is concerned, those libraries don't exist on
my machine anymore. "highgui" is "/usr/lib/libopencv_highgui.so", and so on.
That would explain why my 'fix' didn't work for you.

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vinyl
Wow. This could prove an invaluable tool for teaching. Great UI (love the way
you can build the dataset with a paintbrush), top-quality visualization, wide
choice of algorithms... Much better than R scripts if you want to show basic
algorithms at work to a bunch of students. Many thanks

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Emore
Here's another one, a simple Applet that lets you apply various classification
algorithms on 2D data: <http://www.cs.technion.ac.il/~rani/LocBoost/>

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jwr
Thank you! This is very, very useful. Not only to beginners, but also to those
of us who want to roughly compare the behavior of several algorithms. You can
estimate things like sensitivity to parameters and quickly visualize exactly
how they behave.

It's a very useful tool.

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maeon3
Patch through a black and white video into this tool and watch how the
algorithm tries to find patterns in a 2d reprsentation of a 3d space. Maybe it
can predict where the table ends without full view of the table.

Neat stuff, it is the beginning of machine notion of object permanence.
Filling in the gaps and making educated guesses about what kind of function
controls where a sequence originated from.

