About: Semi-supervised learning

Facets (new session)
Description
Metadata
Settings
- Rule:
- Inverse Functional Properties:
- "Same As":

About: Semi-supervised learning Goto Sponge NotDistinct Permalink

An Entity of Type : owl:Thing, within Data Space : el.dbpedia.org associated with source document(s)

Semi-supervised learning is an approach to 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). It is a special instance of weak supervision.

Attributes	Values
rdfs:label	Semi-supervised learning (en)
rdfs:comment	Semi-supervised learning is an approach to 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). It is a special instance of weak supervision. (en)
sameAs	Semi-supervised learning
dbp:wikiPageUsesTemplate	dbt:Citation_needed dbt:Cite_book dbt:Disputed_inline dbt:Merge_to dbt:Reflist dbt:Sfn dbt:Differentiable_computing dbt:Machine_learning
Subject	Machine learning
prov:wasDerivedFrom	http://en.wikipedia.org/wiki/Semi-supervised_learning?oldid=1049180726&ns=0
Wikipage page ID	2829632 (xsd:integer)
page length (characters) of wiki page	22001 (xsd:nonNegativeInteger)
Wikipage revision ID	1049180726 (xsd:integer)
has abstract	Semi-supervised learning is an approach to 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). It is a special instance of 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. A set of independently identically distributed examples with corresponding labels and unlabeled examples 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 only. The goal of inductive learning is to infer the correct mapping from to . 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. (en)
foaf:isPrimaryTopicOf	http://en.wikipedia.org/wiki/Semi-supervised_learning
is Wikipage redirect of	Semisupervised learning Semi-supervised machine learning Semi supervised learning
is Link from a Wikipage to another Wikipage of	Flow cytometry bioinformatics Generative adversarial network Gibbs sampling Glossary of artificial intelligence Dirichlet-multinomial distribution Discrete Laplace operator Himabindu Lakkaraju History of natural language processing Michael Collins (computational linguist) Naive Bayes classifier Named-entity recognition Classic monolingual word-sense disambiguation Curse of dimensionality Deep learning Hypergraph K-means clustering List of datasets for machine-learning research Co-training Collective classification Constrained clustering Coupled pattern learner Graph Fourier transform Detecting fake news online Feature learning Information engineering (field) Manifold alignment Manifold regularization Learning to rank Maximum-entropy Markov model Multiple kernel learning Natural language processing

Faceted Search & Find service v1.17_git151 as of Feb 20 2025

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3240 as of Nov 11 2024, on Linux (x86_64-ubuntu_focal-linux-gnu), Single-Server Edition (82 GB total memory, 2 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software