| 摘要 |
A method and apparatus comprising a fast and highly effective stochastic algorithm, referred to as Simmered Greedy Optimization (SG(N)), for solving combinatorial optimization problems, including the co-clustering problem comprising simultaneously clustering two finite sets by maximizing the mutual information between the clusterings and deriving maximally predictive feature sets. Co-clustering has found application in many areas, particularly statistical natural language processing and bio-informatics. Provided are results of tests on a suite of statistical natural language problems comparing SG(N) with simulated annealing and a publicly available implementation of co-clustering, wherein using SG(N) provided superior results with far less computation.
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