Our main goal is to provide you with data because you know what you want to do with it. Still, we give some information regarding typical MIR tasks below. We hope to provide snippets of code and benchmarks results to help you getting started. If you want to provide additional information / link to your code / new results / new tasks, please send us an email! We also try to maintain an informal list of publications that use the dataset.
Platform for sharing and evaluation of intelligent algorithms. Data mining data, experiments, datasets, performance analysis, data repository, challenges. Research and applications, prediction. Data mining and machine learning
In the INSEMTIVES game challenge we are looking for colorful, innovative ideas with a twist for new “games with a purpose”. The purpose, of course, is primarily the creation of useful semantic content.
Following a successful first edition, we are pleased to announce the 2nd edition of the Large Scale Hierarchical Text Classification (LSHTC) Pascal Challenge. The LSHTC Challenge is a hierarchical text classification competition, using large datasets. This year’s challenge will increase the scale and the difficulty of the task, using data from Wikipedia (www.wikipedia.org), in addition to the ODP Web directory data (www.dmoz.org).
mendation service which can be called via HTTP by BibSonomy's recommender when a user posts a bookmark or publication. All participating recommenders are called on each posting process, one of them is choosen to actually deliver the results to the user. We can then measure
he Diagnostic Competition is proposed to be the first of a series of international competitions that will be hosted yearly at the International Workshop on Principles of Diagnosis (DX).
I should have realized the danger of stepping into the Wikipedia morass, and the comments on today’s earlier post further indicate my folly in doing so. You know, The New York Times gets things wrong, too. As an argument on a sophisticated level, it’s that all texts are constructs reflecting the attitude of the constructor rather than a verifiable external reality; on a less sophisticated level, it’s that all the other kids are smoking pot, too.
I’ve had enough. I’m bringing it down to this challenge.
R. Jäschke, A. Hotho, F. Mitzlaff, and G. Stumme. Recommender Systems for the Social Web, volume 32 of Intelligent Systems Reference Library, Springer, Berlin/Heidelberg, (2012)
R. Jäschke, A. Hotho, F. Mitzlaff, and G. Stumme. Recommender Systems for the Social Web, volume 32 of Intelligent Systems Reference Library, Springer, Berlin/Heidelberg, (2012)
R. Jäschke, A. Hotho, F. Mitzlaff, and G. Stumme. Recommender Systems for the Social Web, volume 32 of Intelligent Systems Reference Library, Springer, Berlin/Heidelberg, (2012)
R. Jäschke, A. Hotho, F. Mitzlaff, and G. Stumme. Recommender Systems for the Social Web, volume 32 of Intelligent Systems Reference Library, Springer, Berlin/Heidelberg, (2012)
R. Jäschke, A. Hotho, F. Mitzlaff, and G. Stumme. Recommender Systems for the Social Web, volume 32 of Intelligent Systems Reference Library, Springer, Berlin/Heidelberg, (2012)
A. Hotho, D. Benz, R. Jäschke, and B. Krause (Eds.) Workshop at 18th Europ. Conf. on Machine Learning (ECML'08) / 11th Europ. Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'08), (2008)