Results 241 to 250 of about 9,325,572 (289)

Hidden Conditional Random Fields

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007
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.
Trevor Darrell, Louis-Philippe Morency
exaly   +4 more sources

Efficient robust conditional random fields

IEEE Transactions on Image Processing, 2015
Conditional random fields (CRFs) are a flexible yet powerful probabilistic approach and have shown advantages for popular applications in various areas, including text analysis, bioinformatics, and computer vision. Traditional CRF models, however, are incapable of selecting relevant features as well as suppressing noise from noisy original features ...
Dongjin Song, Wei Liu 0005, Tianyi Zhou 0001, Dacheng Tao, David A. Meyer 0001   +4 more
openaire   +3 more sources

Conditional Topic Random Fields

2018
Generative topic models such as LDA are limited by their inability to utilize nontrivial input features to enhance their performance, and many topic models assume that topic assignments of different words are conditionally independent. Some work exists to address the second limitation but no work exists to address both.
Jun Zhu 0001, Eric P. Xing
openaire   +2 more sources

Conditional Random Fields for Intrusion Detection

21st International Conference on Advanced Information Networking and Applications Workshops (AINAW'07), 2007
An intrusion detection system is now an inevitable part of any computer network. With the ever increasing number and diverse type of attacks, including new and previously unseen attacks, the effectiveness of an intrusion detection system is often subjected to testing.
Kapil Kumar Gupta, Baikunth Nath, Kotagiri Ramamohanarao   +2 more
openaire   +1 more source

Variational inference for conditional random fields

2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010
Conditional random fields (CRFs) have been popular for contextual pattern classification. This paper presents two variational inference methods for direct approximation of a conditional probability instead of indirect calculation through Viterbi approximation of a marginal probability.
Chih-Pin Liao, Jen-Tzung Chien
openaire   +2 more sources

Triangular-Chain Conditional Random Fields

IEEE Transactions on Audio, Speech, and Language Processing, 2008
Sequential modeling is a fundamental task in scientific fields, especially in speech and natural language processing, where many problems of sequential data can be cast as a sequential labeling or a sequence classification. In many applications, the two problems are often correlated, for example named entity recognition and dialog act classification ...
Jeong, M, Lee, GG
openaire   +2 more sources