Applying MDL to learn best model granularity
نویسندگان
چکیده
منابع مشابه
Applying MDL to learn best model granularity
The Minimum Description Length (MDL) principle is solidly based on a provably ideal method of inference using Kolmogorov complexity. We test how the theory behaves in practice on a general problem in model selection: that of learning the best model granularity. The performance of a model depends critically on the granularity, for example the choice of precision of the parameters. Too high preci...
متن کاملApplying MDL to Learning Best Model Granularity
The Minimum Description Length (MDL) principle is solidly based on a provably ideal method of inference using Kolmogorov complexity. We test how the theory behaves in practice on a general problem in model selection: that of learning the best model granularity. The performance of a model depends critically on the granularity, for example the choice of precision of the parameters. Too high preci...
متن کامل00 50 62 v 1 2 3 M ay 2 00 0 Applying MDL to Learning Best Model Granularity ∗
The Minimum Description Length (MDL) principle is solidly based on a provably ideal method of inference using Kolmogorov complexity. We test how the theory behaves in practice on a general problem in model selection: that of learning the best model granularity. The performance of a model depends critically on the granularity, for example the choice of precision of the parameters. Too high preci...
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ژورنال
عنوان ژورنال: Artificial Intelligence
سال: 2000
ISSN: 0004-3702
DOI: 10.1016/s0004-3702(00)00034-5