Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

June 5, 2011

MLDemos

Filed under: Machine Learning,Visualization — Patrick Durusau @ 3:22 pm

MLDemos

From the website:

During my PhD I’ve come across a number of machine learning algorithms for classification, regression and clustering. While there is a great number of libraries, source code and binaries for different algorithms, it is always difficult to get a good grasp of what they do. Moreover, one ends up spending a great amount of time just getting the algorithm to display the results in an understandable way. Change the algorithm and you will have to do the work all over again. Some people have tried, and succeeded, to combine several algorithms into a single multi-purpose library, making their libraries extremely useful (you will find many of their names in the acknowledgements below), but still they didn’t solve the problem of visualization and ease of use. Matlab is an easy answer to that, but while extremely easy to use for generating and displaying single instances of data processing (data, results, models), Matlab is annoyingly slow and cumbersome when it comes to creating an interactive GUI. While preparing the exercice sessions for the Applied Machine Learning class at EPFL, I decided to combine the code snippets, example programs and libraries I had at hand into a seamless application where the different algorithms could be compared and studied easily.

This is an awesome piece of work! You can change the parameters and get immediate feedback on the impact of those changes.

Some minor issues (Windows XP, version 0.3.2):

The files in the /help directory have “open with” information set to Adobe Acrobat.

As far as I can tell, the “help” files don’t appear under the help menu or elsewhere.

There is no base directory so files unpack into whatever directory is selected. Suggest creating mldemo directory as target.

Exiting MLDemos is treated as an error and generates and error report for Microsoft.

Documentation, however brief, about the various algorithms and their parameters would be a welcome addition. Perhaps keyed to one or more leading texts on machine learning. That sounds like something that should be contributed by an interested user doesn’t it? 😉

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