regularize

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reg·u·lar·ize

 (rĕg′yə-lə-rīz′)
tr.v. reg·u·lar·ized, reg·u·lar·iz·ing, reg·u·lar·iz·es
To make regular; cause to conform.

reg′u·lar·i·za′tion (-lər-ĭ-zā′shən) n.
reg′u·lar·iz′er n.

regularize

(ˈrɛɡjʊləˌraɪz) or

regularise

vb
(tr) to make regular; cause to conform
ˌregulariˈzation, ˌregulariˈsation n

reg•u•lar•ize

(ˈrɛg yə ləˌraɪz)

v.t. -ized, -iz•ing.
to make regular.
[1615–25]
reg`u•lar•i•za′tion, n.
reg′u•lar•iz`er, n.

regularize


Past participle: regularized
Gerund: regularizing

Imperative
regularize
regularize
Present
I regularize
you regularize
he/she/it regularizes
we regularize
you regularize
they regularize
Preterite
I regularized
you regularized
he/she/it regularized
we regularized
you regularized
they regularized
Present Continuous
I am regularizing
you are regularizing
he/she/it is regularizing
we are regularizing
you are regularizing
they are regularizing
Present Perfect
I have regularized
you have regularized
he/she/it has regularized
we have regularized
you have regularized
they have regularized
Past Continuous
I was regularizing
you were regularizing
he/she/it was regularizing
we were regularizing
you were regularizing
they were regularizing
Past Perfect
I had regularized
you had regularized
he/she/it had regularized
we had regularized
you had regularized
they had regularized
Future
I will regularize
you will regularize
he/she/it will regularize
we will regularize
you will regularize
they will regularize
Future Perfect
I will have regularized
you will have regularized
he/she/it will have regularized
we will have regularized
you will have regularized
they will have regularized
Future Continuous
I will be regularizing
you will be regularizing
he/she/it will be regularizing
we will be regularizing
you will be regularizing
they will be regularizing
Present Perfect Continuous
I have been regularizing
you have been regularizing
he/she/it has been regularizing
we have been regularizing
you have been regularizing
they have been regularizing
Future Perfect Continuous
I will have been regularizing
you will have been regularizing
he/she/it will have been regularizing
we will have been regularizing
you will have been regularizing
they will have been regularizing
Past Perfect Continuous
I had been regularizing
you had been regularizing
he/she/it had been regularizing
we had been regularizing
you had been regularizing
they had been regularizing
Conditional
I would regularize
you would regularize
he/she/it would regularize
we would regularize
you would regularize
they would regularize
Past Conditional
I would have regularized
you would have regularized
he/she/it would have regularized
we would have regularized
you would have regularized
they would have regularized
ThesaurusAntonymsRelated WordsSynonymsLegend:
Verb1.regularize - bring into conformity with rules or principles or usage; impose regulations; "We cannot regulate the way people dress"; "This town likes to regulate"
standardize, standardise - cause to conform to standard or norm; "The weights and measures were standardized"
decide, make up one's mind, determine - reach, make, or come to a decision about something; "We finally decided after lengthy deliberations"
district, zone - regulate housing in; of certain areas of towns
2.regularize - make regular or more regular; "regularize the heart beat with a pace maker"
even, even out - make even or more even
arrange, set up - put into a proper or systematic order; "arrange the books on the shelves in chronological order"
Translations

regularize

[ˈregjʊləraɪz] VT (= standardize) [+ activities, procedure] → regularizar, estandarizar; (= make official) [+ situation] → formalizar, regularizar

regularize

vt breathing, serviceregulieren; situation, relationshipnormalisieren; menstrual periodregeln

regularize

[ˈrɛgjʊləˌraɪz] vtregolarizzare
References in periodicals archive ?
with Prejudice Remover Regularizer, in MACHINE LEARNING AND KNOWLEDGE
[43] proposed a novel MKL algorithm with a group lasso regularizer, called group lasso regularized MKL (GL-MKL), for heterogeneous feature fusion and variable selection.
In addition, we may combine our constraint learning objective with a standard classification loss term (over the labeled data), which acts as an additional regularizer. This process can be interpreted as a new semisupervised algorithm for structured prediction problems, such as tracking, object detection, and pose estimation.
By the properties of regularizer [J.sub.[epsilon]], for m > 5/2, we have
The last term, [[parallel][delta](x - [x.sub.n])[parallel].sup.2], is a regularizer for numerical stability, preventing the solution from drifting away from a reasonable initial guess.
In our objectives, the meaning of [gamma] can be preserved as in the BEGAN model because we update [k.sub.i] regardless of the regularizer [L.sub.Q].
The Least Absolute Shrinkage and Selection Operator (LASSO) is a Linear Discriminant Analysis (LDA) variation that uses the [l.sub.1]-norm as regularizer:
The authors in [6] proposed nonlocal regularization; their regularizer was designed to selectively consider and use anatomical information from other sources, such as CT, only when the information was reliable.
Nonlinear embedding algorithms are popular for use with shallow semisupervised learning techniques such as kernel methods, but they can be applied to deep multilayer architectures as well, as a regularizer either at the output layer or on the hidden layers of the architecture [31].
Therefore, a linear regression model with L1 and L2 prior as a regularizer, called adaptive elastic net (AEN) regression [30], was developed to predict the usage intensity and the degree of imbalance.
Total variation (TV) [15] is another regularizer which overcomes the shortcoming of Tikhonov method and is widely used in image restoration tasks.
The first term, F, is called the data fidelity term, while the second term, G, is a regularizer. Many different schemes respond to this structure.