The goal of SIGMA is to provide a group of parallel machine-learning algorithms that can meet the requirements of research work and applications typically with large-scale data or features. The tool kit includes more than 10 algorithms and it makes them run on single multicore machine or on a HPC cluster with hundreds of machines and thousands of CPU cores running. Release history: version 1.0: 2009/Oct/12: a basic version with algorithms. version 1.1: 2010/Feb/01: add up to ten algorithms and pass our internal testing. Contactor: kalana attygalle (http://research.microsoft.com/en-us/people/wzchen/ )
An integrated development environment for writing and checking TLA+ specifications.
This research code is implementation of the scheme proposed in our CVPR paper “Active Learning for Large Multi-Class Problems.” This is a MATLAB prototype that researchers will find useful for comparison and benchmarking.
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