semi-weak key - перевод на русский
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semi-weak key - перевод на русский

MACHINE LEARNING APPROACH WHERE NOISY, LIMITED, OR IMPRECISE SOURCES ARE USED TO PROVIDE SUPERVISION SIGNAL FOR LABELING LARGE AMOUNTS OF TRAINING DATA IN A SUPERVISED LEARNING SETTING
Semi-supervised learning; Semi supervised learning; Semisupervised learning; Weak supervison; Semi-supervised machine learning; Semi-Supervised Learning
  • clustering]] and then labeling the clusters with the labeled data, pushing the decision boundary away from high-density regions, or learning an underlying one-dimensional manifold where the data reside.

semi-weak key      
полувырожденный ключ полувырожденный ключ
weak interaction         
  • electron antineutrino]].
  • W boson-}}}}).
  • Pion+}} decay through the weak interaction
  • spin]]. Note the lack of reflective symmetry between the states.
  • A diagram depicting the decay routes due to the charged weak interaction and some indication of their likelihood. The intensity of the lines is given by the [[CKM parameters]].
FUNDAMENTAL INTERACTION RESPONSIBLE FOR BETA DECAY AND NUCLEAR FISSION
Weak force; Weak nuclear interaction; Weak decay; Weak interactions; Weak Interaction; Weak Force; Quantum flavordynamics; Flavordynamics; Weak nuclear force; Weak nuclear interactions; Weak Law of Action and Reaction; Weak-Nuclear Force; Weak Nuclear Force; Nuclear weak force; Flavodynamics; Weak Nuclear Forces; Wesak force; V-A theory; W boson exchange; V–A theory; V−A theory

['wi:kintə'rækʃ(ə)n]

общая лексика

слабое взаимодействие

физика

слабое бета-распадное взаимодействие

major key         
  • ii-V<sup>7</sup>-I progression]] in C [[File:Ii-V-I turnaround in C.mid]]
TONIC NOTE AND CHORD OF A MUSICAL PIECE
Major key; Key (Music); Musical key; Major Key; Key of D; Musical keys; Key coloration; Key relationship; Musical Key; Music key; Minor-key; Minor–key; Major-key; Key of E
мажорная тональность

Определение

Ки-Уэст
(Key West)

город на Юго-Востоке США, в штате Флорида, на о. Ки-Уэст, соединённый автодорогой (проложенной через цепь коралловых островов по мостам и дамбам) с полуостровом Флорида. 27,6 тыс. жителей (1970). Рыболовство. Зимний морской курорт.

Википедия

Weak supervision

Weak supervision, also called semi-supervised learning, is a branch of machine learning that combines a small amount of labeled data with a large amount of unlabeled data during training. Semi-supervised learning falls between unsupervised learning (with no labeled training data) and supervised learning (with only labeled training data). Semi-supervised learning aims to alleviate the issue of having limited amounts of labeled data available for training.

Semi-supervised learning is motivated by problem settings where unlabeled data is abundant and obtaining labeled data is expensive. Other branches of machine learning that share the same motivation but follow different assumptions and methodologies are active learning and weak supervision. Unlabeled data, when used in conjunction with a small amount of labeled data, can produce considerable improvement in learning accuracy. The acquisition of labeled data for a learning problem often requires a skilled human agent (e.g. to transcribe an audio segment) or a physical experiment (e.g. determining the 3D structure of a protein or determining whether there is oil at a particular location). The cost associated with the labeling process thus may render large, fully labeled training sets infeasible, whereas acquisition of unlabeled data is relatively inexpensive. In such situations, semi-supervised learning can be of great practical value. Semi-supervised learning is also of theoretical interest in machine learning and as a model for human learning.

More formally, semi-supervised learning assumes a set of l {\displaystyle l} independently identically distributed examples x 1 , , x l X {\displaystyle x_{1},\dots ,x_{l}\in X} with corresponding labels y 1 , , y l Y {\displaystyle y_{1},\dots ,y_{l}\in Y} and u {\displaystyle u} unlabeled examples x l + 1 , , x l + u X {\displaystyle x_{l+1},\dots ,x_{l+u}\in X} are processed. Semi-supervised learning combines this information to surpass the classification performance that can be obtained either by discarding the unlabeled data and doing supervised learning or by discarding the labels and doing unsupervised learning.

Semi-supervised learning may refer to either transductive learning or inductive learning. The goal of transductive learning is to infer the correct labels for the given unlabeled data x l + 1 , , x l + u {\displaystyle x_{l+1},\dots ,x_{l+u}} only. The goal of inductive learning is to infer the correct mapping from X {\displaystyle X} to Y {\displaystyle Y} .

Intuitively, the learning problem can be seen as an exam and labeled data as sample problems that the teacher solves for the class as an aid in solving another set of problems. In the transductive setting, these unsolved problems act as exam questions. In the inductive setting, they become practice problems of the sort that will make up the exam.

It is unnecessary (and, according to Vapnik's principle, imprudent) to perform transductive learning by way of inferring a classification rule over the entire input space; however, in practice, algorithms formally designed for transduction or induction are often used interchangeably.

Как переводится semi-weak key на Русский язык