People : eric : projects

We explore algorithms for implementing large scale learning algorithms as parallel computation. We are currently developing parallelization approaches for increasing the ability of SVM (Support Vector Machines) to solve large-scale problems. As target systems, we consider shared memory processors, clusters of processors, vector processors, and SIMD (Single Instruction Multiple Data) processors. On a given system the speed of an SVM is limited by the compute performance of the processor as well as by the size of the memory. Efficient parallelizations have to overcome both of these limitations while not getting bogged down in communication overhead.

MiLDe is an integrated development environment with a suite of machine learning tools including SVMs, Neural Networks (using the Torch package), image analysis, numeric functions and dataset handling.