The presentations tells the story of clusterlib an open source package from the problem statement to a first grade an open source library. Awesome tools are also presented for software projects. The goal of the clusterlib is to ease the creation, launch and management of embarrassingly parallel jobs on supercomputers with schedulers such as SLURM and SGE.]]>

The presentations tells the story of clusterlib an open source package from the problem statement to a first grade an open source library. Awesome tools are also presented for software projects. The goal of the clusterlib is to ease the creation, launch and management of embarrassingly parallel jobs on supercomputers with schedulers such as SLURM and SGE.]]>

We first present the Python programming language and the NumPy package for scientific computing. Then, we devise a digit recognition system highlighting the scikit-learn package.]]>

We first present the Python programming language and the NumPy package for scientific computing. Then, we devise a digit recognition system highlighting the scikit-learn package.]]>

Git is an amazing source control version system for writing. It allows you keep track of the modification and collaborate with a large number of people. Platforms such as bitbucket or github make it straightforward to use.]]>

Git is an amazing source control version system for writing. It allows you keep track of the modification and collaborate with a large number of people. Platforms such as bitbucket or github make it straightforward to use.]]>

Random forests are effective supervised learning methods applicable to large-scale datasets. However, the space complexity of tree ensembles, in terms of their total number of nodes, is often prohibitive, specially in the context of problems with very high-dimensional input spaces. We propose to study their compressibility by applying a L1-based regularization to the set of indicator functions defined by all their nodes. We show experimentally that preserving or even improving the model accuracy while significantly reducing its space complexity is indeed possible. Link to the paper http://orbi.ulg.ac.be/handle/2268/124834]]>

Random forests are effective supervised learning methods applicable to large-scale datasets. However, the space complexity of tree ensembles, in terms of their total number of nodes, is often prohibitive, specially in the context of problems with very high-dimensional input spaces. We propose to study their compressibility by applying a L1-based regularization to the set of indicator functions defined by all their nodes. We show experimentally that preserving or even improving the model accuracy while significantly reducing its space complexity is indeed possible. Link to the paper http://orbi.ulg.ac.be/handle/2268/124834]]>

We adapt the idea of random projections applied to the out- put space, so as to enhance tree-based ensemble methods in the context of multi-label classification. We show how learning time complexity can be reduced without affecting computational complexity and accuracy of predictions. We also show that random output space projections may be used in order to reach different bias-variance tradeoffs, over a broad panel of benchmark problems, and that this may lead to improved accuracy while reducing significantly the computational burden of the learning stage. Link to the paper http://orbi.ulg.ac.be/handle/2268/172146 Souce code available at https://github.com/arjoly/random-output-trees]]>

We adapt the idea of random projections applied to the out- put space, so as to enhance tree-based ensemble methods in the context of multi-label classification. We show how learning time complexity can be reduced without affecting computational complexity and accuracy of predictions. We also show that random output space projections may be used in order to reach different bias-variance tradeoffs, over a broad panel of benchmark problems, and that this may lead to improved accuracy while reducing significantly the computational burden of the learning stage. Link to the paper http://orbi.ulg.ac.be/handle/2268/172146 Souce code available at https://github.com/arjoly/random-output-trees]]>

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