Deterministic noise
In (supervised) machine learning, specifically when learning from data, there are situations when the data values cannot be modeled. This may arise if there are random fluctuations or measurement errors in the data which are not modeled, and can be appropriately called stochastic noise; or, when the phenomenon being modeled (or learned) is too complex, and so the data contains this added complexity that is not modeled. This added complexity in the data has been called deterministic noise. Though these two types of noise arise from different causes, their adverse effect on learning is similar. The overfitting occurs because the model attempts to fit the (stochastic or deterministic) noise (that part of the data that it cannot model) at the expense of fitting that part of the data which it c
Link from a Wikipage to another Wikipage
primaryTopic
Deterministic noise
In (supervised) machine learning, specifically when learning from data, there are situations when the data values cannot be modeled. This may arise if there are random fluctuations or measurement errors in the data which are not modeled, and can be appropriately called stochastic noise; or, when the phenomenon being modeled (or learned) is too complex, and so the data contains this added complexity that is not modeled. This added complexity in the data has been called deterministic noise. Though these two types of noise arise from different causes, their adverse effect on learning is similar. The overfitting occurs because the model attempts to fit the (stochastic or deterministic) noise (that part of the data that it cannot model) at the expense of fitting that part of the data which it c
has abstract
In (supervised) machine learni ...... ually improve the performance.
@en
Wikipage page ID
35,605,171
page length (characters) of wiki page
Wikipage revision ID
991,695,997
Link from a Wikipage to another Wikipage
wikiPageUsesTemplate
subject
hypernym
comment
In (supervised) machine learni ...... at part of the data which it c
@en
label
Deterministic noise
@en