Scaling up inductive learning with massive parallelism
نویسندگان
چکیده
منابع مشابه
Scaling Up Inductive Learning with MassiveParallelismFOSTER
Machine learning programs need to scale up to very large data sets for several reasons, including increasing accuracy and discovering infrequent special cases. Current inductive learners perform well with hundreds or thousands of training examples, but in some cases, up to a million or more examples may be necessary to learn important special cases with conndence. These tasks are infeasible for...
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ژورنال
عنوان ژورنال: Machine Learning
سال: 1996
ISSN: 0885-6125,1573-0565
DOI: 10.1007/bf00116898