Name
Title | ||
|---|---|---|
Kernel Embeddings of Conditional Distributions: A Unified Kernel Framework for Nonparametric Inference in Graphical Models. |
Abstract | ||
|---|---|---|
Many modern applications of signal processing and machine learning, ranging from computer vision to computational biology, require the analysis of large volumes of high-dimensional continuous-valued measurements. Complex statistical features are commonplace, including multimodality, skewness, and rich dependency structures. Such problems call for a flexible and robust modeling framework that can t... |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/MSP.2013.2252713 | IEEE Signal Processing Magazine |
Keywords | Field | DocType |
Machine learning,Learning systems,Kernel,Computer vision,Computational biology,Parametric statistics | Algorithmic learning theory,Conditional probability distribution,Kernel embedding of distributions,Computer science,Theoretical computer science,Tree kernel,Parametric statistics,Artificial intelligence,Graphical model,Statistical theory,Kernel method,Machine learning | Journal |
Volume | Issue | ISSN |
30 | 4 | 1053-5888 |
Citations | PageRank | References |
38 | 1.79 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Le Song | 1 | 2437 | 159.27 |
| kenji fukumizu | 2 | 1683 | 158.91 |
| Arthur Gretton | 3 | 3638 | 226.18 |