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CROSSWORD
ANSWER

penalized

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The answer PENALIZED has 1 possible clue(s) in existing crosswords.

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The word PENALIZED is VALID in some board games. Check PENALIZED in word games in Scrabble, Words With Friends, see scores, anagrams etc.

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Definitions of penalized in various dictionaries:

verb - impose a penalty on

verb - to subject to a penalty

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Possible Crossword Clues
Docked

Last Seen in these Crosswords & Puzzles
Oct 4 2016 Jonesin'

Possible Dictionary Clues
Simple past tense and past participle of penalize.
punished by the imposition of a penalty
Subject to a penalty or punishment.
Put at an unfair disadvantage.
subject to a penalty or punishment.
put at an unfair disadvantage.

Penalized might refer to
Regularized least squares (RLS) is a family of methods for solving the least-squares problem while using regularization to further constrain the resulting solution. * RLS is used for two main reasons. The first comes up when the number of variables in the linear system exceeds the number of observations. In such settings, the ordinary least-squares problem is ill-posed and is therefore impossible to fit because the associated optimization problem has infinitely many solutions. RLS allows the introduction of further constraints that uniquely determine the solution. * The second reason that RLS is used occurs when the number of variables does not exceed the number of observations, but the learned model suffers from poor generalization. RLS can be used in such cases to improve the generalizability of the model by constraining it at training time. This constraint can either force the solution to be "sparse" in some way or to reflect other prior knowledge about the problem such as information about correlations between features. A Bayesian understanding of this can be reached by showing that RLS methods are often equivalent to priors on the solution to the least-squares problem.

Penalized might refer to

Regularized least squares (RLS) is a family of methods for solving the least-squares problem while using regularization to further constrain the resulting solution.
* RLS is used for two main reasons. The first comes up when the number of variables in the linear system exceeds the number of observations. In such settings, the ordinary least-squares problem is ill-posed and is therefore impossible to fit because the associated optimization problem has infinitely many solutions. RLS allows the introduction of further constraints that uniquely determine the solution.
* The second reason that RLS is used occurs when the number of variables does not exceed the number of observations, but the learned model suffers from poor generalization. RLS can be used in such cases to improve the generalizability of the model by constraining it at training time. This constraint can either force the solution to be "sparse" in some way or to reflect other prior knowledge about the problem such as information about correlations between features. A Bayesian understanding of this can be reached by showing that RLS methods are often equivalent to priors on the solution to the least-squares problem.

Related Answers
LAIDTO
MOORED

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