Difference between revisions of "Inductive bias"

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In [[Bayesian]] framework, inductive bias is encoded in the [[prior distribution]].
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"Inductive bias" refers to your suspicion that if the sun has risen for the last billion days in a row, then it may rise tomorrow as well.  Since it is [[logically possible]] that the laws of physics will arbitrarily cease to work and that the sun will *not* rise tomorrow, coming to this conclusion requires an inductively biased [[prior]].
  
==See also==
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This sort of bias is not a bad thing - without "inductive bias" you can't draw any conclusion at all from the data.  It's just a different technical meaning attached to the same word.
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===Primary article===
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*[http://lesswrong.com/lw/hg/inductive_bias/ "Inductive Bias"] by [[Eliezer Yudkowsky]]
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===See also===
 
*[[Prior distribution]]
 
*[[Prior distribution]]
 
*[[Statistical bias]]
 
*[[Statistical bias]]
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*[[Cognitive bias]]
  
==Footnotes==
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===Footnotes===
 
<references/>
 
<references/>
 
==Blog posts==
 
*[http://lesswrong.com/lw/hg/inductive_bias/ "Inductive Bias"] by [[Eliezer Yudkowsky]]
 
 
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Revision as of 08:57, 5 June 2009

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The inductive bias of a learning algorithm is the set of assumptions that the learner uses to predict outputs given inputs that it has not encountered

Tom Mitchell[1]

"Inductive bias" refers to your suspicion that if the sun has risen for the last billion days in a row, then it may rise tomorrow as well. Since it is logically possible that the laws of physics will arbitrarily cease to work and that the sun will *not* rise tomorrow, coming to this conclusion requires an inductively biased prior.

This sort of bias is not a bad thing - without "inductive bias" you can't draw any conclusion at all from the data. It's just a different technical meaning attached to the same word.

Primary article

See also

Footnotes