About: Manifold regularization

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

About: Manifold regularization Goto Sponge NotDistinct Permalink

An Entity of Type : dbo:TopicalConcept, within Data Space : dbpedia.demo.openlinksw.com associated with source document(s)
QRcode icon

http://dbpedia.demo.openlinksw.com/describe/?url=http%3A%2F%2Fdbpedia.org%2Fresource%2FManifold_regularization&invfp=IFP_OFF&sas=SAME_AS_OFF

In machine learning, Manifold regularization is a technique for using the shape of a dataset to constrain the functions that should be learned on that dataset. In many machine learning problems, the data to be learned do not cover the entire input space. For example, a facial recognition system may not need to classify any possible image, but only the subset of images that contain faces. The technique of manifold learning assumes that the relevant subset of data comes from a manifold, a mathematical structure with useful properties. The technique also assumes that the function to be learned is smooth: data with different labels are not likely to be close together, and so the labeling function should not change quickly in areas where there are likely to be many data points. Because of this

Attributes	Values
rdf:type	topical concept
rdfs:label	Manifold regularization (en)
rdfs:comment	In machine learning, Manifold regularization is a technique for using the shape of a dataset to constrain the functions that should be learned on that dataset. In many machine learning problems, the data to be learned do not cover the entire input space. For example, a facial recognition system may not need to classify any possible image, but only the subset of images that contain faces. The technique of manifold learning assumes that the relevant subset of data comes from a manifold, a mathematical structure with useful properties. The technique also assumes that the function to be learned is smooth: data with different labels are not likely to be close together, and so the labeling function should not change quickly in areas where there are likely to be many data points. Because of this (en)
foaf:depiction
dcterms:subject	Machine learning
Wikipage page ID	48777199 (xsd:integer)
Wikipage revision ID	1097471695 (xsd:integer)
Link from a Wikipage to another Wikipage	Elastic net regularization Meshfree methods Kernel methods Bergman kernel Curse of dimensionality Medical imaging Norm (mathematics) Representer theorem MATLAB Machine learning Kernel method Machine learning C++ Document classification Duality (optimization) Laplacian matrix Least squares Spectral graph theory Facial recognition system Finite difference method Regression analysis Regularization (mathematics) Hinge loss Hyperparameter optimization K-nearest neighbors algorithm Laplace operator Support vector machines Co-training Tikhonov regularization Transduction (machine learning) Differential geometry Divergence Manifold Spectroscopy Loss function Mean squared error Semi-supervised learning Statistical classification Manifold hypothesis Well-posed problem Overfitting Manifold learning Supervised learning Linear program Reproducing kernel Hilbert spaces Probability density Brain–computer interfaces
Link from a Wikipage to an external page	http://manifold.cs.uchicago.edu/manifold_regularization/software.html http://www.dii.unisi.it/~melacci/lapsvmp/ http://dlib.net/ml.html
sameAs	Manifold regularization Manifold regularization
dbp:wikiPageUsesTemplate	dbt:Citation_needed dbt:Reflist
thumbnail	wiki-commons:Special:FilePath/Example_of_unlabeled_data_in_semisupervised_learning.png?width=300
has abstract	In machine learning, Manifold regularization is a technique for using the shape of a dataset to constrain the functions that should be learned on that dataset. In many machine learning problems, the data to be learned do not cover the entire input space. For example, a facial recognition system may not need to classify any possible image, but only the subset of images that contain faces. The technique of manifold learning assumes that the relevant subset of data comes from a manifold, a mathematical structure with useful properties. The technique also assumes that the function to be learned is smooth: data with different labels are not likely to be close together, and so the labeling function should not change quickly in areas where there are likely to be many data points. Because of this assumption, a manifold regularization algorithm can use unlabeled data to inform where the learned function is allowed to change quickly and where it is not, using an extension of the technique of Tikhonov regularization. Manifold regularization algorithms can extend supervised learning algorithms in semi-supervised learning and transductive learning settings, where unlabeled data are available. The technique has been used for applications including medical imaging, geographical imaging, and object recognition. (en)
gold:hypernym	Technique
prov:wasDerivedFrom	wikipedia-en:Manifold_regularization?oldid=1097471695&ns=0
page length (characters) of wiki page	28029 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Manifold_regularization
is rdfs:seeAlso of	Nonlinear dimensionality reduction
is Link from a Wikipage to another Wikipage of	Bergman kernel Machine learning Manifold hypothesis Weak supervision Outline of machine learning
is foaf:primaryTopic of	wikipedia-en:Manifold_regularization

Faceted Search & Find service v1.17_git139 as of Feb 29 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 08.03.3330 as of Mar 19 2024, on Linux (x86_64-generic-linux-glibc212), Single-Server Edition (378 GB total memory, 57 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software