| Papers | Software | Licensing
Many different machine learning algorithms exist that
can easily be used off the shelf, many of these methods are implemented
in the open source WEKA
package. However, each of these algorithms have their own
hyperparameters that can drastically change their performance, and
there are a staggeringly large number of possible alternatives overall.
Auto-WEKA considers the problem of simultaneously selecting a learning
algorithm and setting its hyperparameters, going beyond previous
methods that address these issues in isolation. Auto-WEKA does this
using a fully automated approach, leveraging recent innovations in
Bayesian optimization. Our hope is that Auto-WEKA will help non-expert
users to more effectively identify machine learning algorithms and
hyperparameter settings appropriate to their applications, and hence to
achieve improved performance.
|01 March, 2016
||Auto-WEKA is back with a vengeance! We have just released version 1.0 with a new interface that integrates into WEKA's package manager.
|12 November, 2013
||Bug fixes, updated documentation, more usability tweaks
|9 August, 2013
||New version of Auto-WEKA code, emphasis on usability
|5 June, 2013
||Added datasets, updated Auto-WEKA code
|2 March, 2013
||Initial release of Auto-WEKA
Combined Selection and Hyperparameter Optimization of Classification
Chris Thornton, Frank Hutter, Holger Hoos, and Kevin Leyton-Brown.
In Proc. of KDD 2013, 2013.
[The main Auto-WEKA Paper]
Auto-WEKA is released under the GNU General Public License version 3.
Please send any questions, concerns or comments to Lars Kotthoff.