The need for interpretability biases
J Fürnkranz, T Kliegr - International Symposium on Intelligent Data …, 2018 - Springer
In his seminal paper, Mitchell has defined bias as “any basis for choosing one
generalization over another, other than strict consistency with the observed training
instances”, such as the choice of the hypothesis language or any form of preference relation
between its elements. The most commonly used form is a simplicity bias, which prefers
simpler hypotheses over more complex ones, even in cases when the latter provide a better
fit to the data. Such a bias not only helps to avoid overfitting, but is also commonly …
generalization over another, other than strict consistency with the observed training
instances”, such as the choice of the hypothesis language or any form of preference relation
between its elements. The most commonly used form is a simplicity bias, which prefers
simpler hypotheses over more complex ones, even in cases when the latter provide a better
fit to the data. Such a bias not only helps to avoid overfitting, but is also commonly …
The Need for Interpretability Biases
J Fürnkranz¹, T Kliegr - Advances in Intelligent Data Analysis XVII …, 2018 - books.google.com
In his seminal paper, Mitchell has defined bias as “any basis for choosing one
generalization over another, other than strict consistency with the observed training
instances", such as the choice of the hypothesis language or any form of preference relation
between its elements. The most commonly used form is a simplicity bias, which prefers
simpler hypotheses over more complex ones, even in cases when the latter provide a better
fit to the data. Such a bias not only helps to avoid overfitting, but is also commonly …
generalization over another, other than strict consistency with the observed training
instances", such as the choice of the hypothesis language or any form of preference relation
between its elements. The most commonly used form is a simplicity bias, which prefers
simpler hypotheses over more complex ones, even in cases when the latter provide a better
fit to the data. Such a bias not only helps to avoid overfitting, but is also commonly …
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