About: Kernel embedding of distributions

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

About: Kernel embedding of distributions Goto Sponge NotDistinct Permalink

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

http://dbpedia.org/describe/?url=http%3A%2F%2Fdbpedia.org%2Fresource%2FKernel_embedding_of_distributions&graph=http%3A%2F%2Fdbpedia.org&graph=http%3A%2F%2Fdbpedia.org

In machine learning, the kernel embedding of distributions (also called the kernel mean or mean map) comprises a class of nonparametric methods in which a probability distribution is represented as an element of a reproducing kernel Hilbert space (RKHS). A generalization of the individual data-point feature mapping done in classical kernel methods, the embedding of distributions into infinite-dimensional feature spaces can preserve all of the statistical features of arbitrary distributions, while allowing one to compare and manipulate distributions using Hilbert space operations such as inner products, distances, projections, linear transformations, and spectral analysis. This learning framework is very general and can be applied to distributions over any space on which a sensible kernel

Attributes	Values
rdfs:label	Kernel embedding of distributions (en)
rdfs:comment	In machine learning, the kernel embedding of distributions (also called the kernel mean or mean map) comprises a class of nonparametric methods in which a probability distribution is represented as an element of a reproducing kernel Hilbert space (RKHS). A generalization of the individual data-point feature mapping done in classical kernel methods, the embedding of distributions into infinite-dimensional feature spaces can preserve all of the statistical features of arbitrary distributions, while allowing one to compare and manipulate distributions using Hilbert space operations such as inner products, distances, projections, linear transformations, and spectral analysis. This learning framework is very general and can be applied to distributions over any space on which a sensible kernel (en)
dcterms:subject	Theory of probability distributions Machine learning
Wikipage page ID	41370976 (xsd:integer)
Wikipage revision ID	1073073352 (xsd:integer)
Link from a Wikipage to another Wikipage	Belief propagation Theory of probability distributions Probability distribution Entropy estimation Minimax estimator Time series Kernel methods Nonparametric Basis function Bernhard Schölkopf Bounded function Hyperparameter Joint probability distribution Bias of an estimator Bergman kernel Curse of dimensionality Vector (mathematics and physics) Incomplete Cholesky factorization Point estimation Pearson correlation Compact space Continuous function Cross-covariance Network theory Mixture model Separable space Entropy Entropy (information theory) Gaussian process Gramian matrix Graph (discrete mathematics) Mutual information Concentration of measure Cross-validation (statistics) Location parameter Machine learning Sinc Statistics Cluster analysis Dense set Feature selection Identity matrix Kernel density estimation Kernel principal component analysis Standard basis Spectral theory String (computer science) Machine learning Domain adaptation Linear map Linear subspace Exponential family Collocation method Bregman divergence Dimensionality reduction Graphical model Hilbert–Schmidt integral operator Hilbert–Schmidt operator Hat matrix Regularization (mathematics) Hidden Markov Model Hilbert space Tensor Tensor product Dynamical systems Characteristic function (probability theory) Support (mathematics) Support vector machine Hidden Markov model Tikhonov regularization Manifold Positive-definite matrix Independence (probability theory) Independent and identically distributed random variables Inner product Kronecker delta Kullback–Leibler divergence Orthogonal matrix Radial basis function Markov random field Scale parameter Similarity measure Conditional random fields Kernel trick Linear transformation Multiple-instance learning Well-posed problem Overfitting Markov Random Field Kernel function Conditional distribution
Link from a Wikipage to an external page	http://alex.smola.org/ http://www.gatsby.ucl.ac.uk/~gretton/ https://bitbucket.org/szzoli/ite/

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 (61 GB total memory, 51 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software