mlf 2.0 Grid-Enabled

Recently, we took mlf 2.0 to data mining teams, which want to apply comprehensive multi-method machine learning providing access to their multi-core power. Mlf 2 was enhanced by new functions which allow parallell data mining tasks; the cross-model-validation and the run of the Model Explorer. Kerne methods include now Support Vector Machines and Gaussian Process Regression and a feature that allows the integration of user defined algorithms, new meta-learning algorithms for feature selection, boosting and mixtures of experts. It also adds modeling of time series data. This functionality extends mlf's vast variety of methods for supervised and unsupervised learning, with methods empowered by Fuzzy Logic to make models like decision trees and rules not only understandable but also computational.
Mlf 2 exploits Mathematica 7's built-in parallelism which increases the power of mlf by adding extra computational kernels either Local in a multi-core computer or as Server in a network-managed pool of computational kernels. It is set up to allow seamless scaling to networks, clusters, grids and clouds.
Find buggy parts of software by machine learning? Which data sources? Which methods? As result of challenging investigations on large industrial systems. The best combination in the picture above: static code analysis and fuzzy-ID3 in mlf.

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