supervised study - ترجمة إلى ألماني
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supervised study - ترجمة إلى ألماني

MACHINE LEARNING TASK OF LEARNING A FUNCTION THAT MAPS AN INPUT TO AN OUTPUT BASED ON EXAMPLE INPUT-OUTPUT PAIRS
Supervised classification; Supervised machine learning; Supervised Machine Learning; Fully-supervised machine learning; Applications of supervised learning; Algorithms for supervised learning; Generative training

supervised study      
Lernen unter Beaufsichtigung
study hall         
A PLACE AND/OR TIME DURING THE SCHOOL DAY WHERE STUDENTS ARE ASSIGNED TO STUDY WHEN THEY ARE NOT SCHEDULED FOR AN ACADEMIC CLASS
Study Hall
Studienzimmer; Klassenraum; Vorlesungshalle
case study         
  • Engineering students participate in a case study competition.
INTENSIVE ANALYSIS OF AN INDIVIDUAL UNIT STRESSING DEVELOPMENTAL FACTORS IN RELATION TO CONTEXT
Case-studies; Case studies; Case-study; Case example; Teaching cases; Case Study Analysis; Cross-case analysis; Case study research; Sampling (case studies); Case selection; Case writing; Case study method; Empirical inquiry; Case article
Fallstudie, detaillierte Studie einer bestimmten Situation oder eines bestimmten Falles; Aufzeichnung von Problemen und Schwierigkeiten einer Person und wie diese gelöst wurden (beim Arzt)

تعريف

study hall
¦ noun N. Amer. the period of time in a school curriculum designated for study and the preparation of homework.

ويكيبيديا

Supervised learning

Supervised learning (SL) is a machine learning paradigm for problems where the available data consists of labeled examples, meaning that each data point contains features (covariates) and an associated label. The goal of supervised learning algorithms is learning a function that maps feature vectors (inputs) to labels (output), based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way (see inductive bias). This statistical quality of an algorithm is measured through the so-called generalization error.