Predictability, Complexity, and Learning

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چکیده

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Predictability, Complexity, and Learning

We define predictive information I(pred)(T) as the mutual information between the past and the future of a time series. Three qualitatively different behaviors are found in the limit of large observation times T:I(pred)(T) can remain finite, grow logarithmically, or grow as a fractional power law. If the time series allows us to learn a model with a finite number of parameters, then I(pred)(T) ...

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We define predictive information Ipred(T ) as the mutual information between the past and the future of a time series. Three qualitatively different behaviors are found in the limit of large observation times T : Ipred(T ) can remain finite, grow logarithmically, or grow as a fractional power law. If the time series allows us to learn a model with a finite number of parameters, then Ipred(T ) g...

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We define predictive information Ipred(T ) as the mutual information between the past and the future of a time series. Three qualitatively different behaviors are found in the limit of large observation times T : Ipred(T ) can remain finite, grow logarithmically, or grow as a fractional power law. If the time series allows us to learn a model with a finite number of parameters, then Ipred(T ) g...

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Determinism, Complexity, and Predictability in Computer Performance

Computers are deterministic dynamical systems [1]. Among other things, that implies that one should be able to use deterministic forecast rules to predict their behavior. That statement is sometimes—but not always—true. The memory and processor loads of some simple programs are easy to predict, for example, but those of more-complex programs like gcc are not. The goal of this paper is to determ...

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ژورنال

عنوان ژورنال: Neural Computation

سال: 2001

ISSN: 0899-7667,1530-888X

DOI: 10.1162/089976601753195969