Hidden conditional random fields

被引：316
|
作者：
Quattoni, Ariadna [1 ]
Wang, Sybor [1 ]
Morency, Louis-Philippe [1 ]
Collins, Michael [1 ]
Darrell, Trevor [1 ]
机构：
[1] MIT, Comp Sci & Artificial Intelligence Lab, Cambridge, MA 02139 USA
来源：
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 2007年 / 29卷 / 10期
关键词：
object recognition; model; supervised learning; classification;
D O I：
10.1109/TPAMI.2007.1124
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We present a discriminative latent variable model for classification problems in structured domains where inputs can be represented by a graph of local observations. A hidden-state Conditional Random Field framework learns a set of latent variables conditioned on local features. Observations need not be independent and may overlap in space and time.
引用
收藏
页码：1848 / 1853
页数：6
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