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Hidden Conditional Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007We 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.
Ariadna Quattoni +4 more
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Conditional Topic Random Fields [PDF]
, 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
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Efficient robust conditional random fields
IEEE Transactions on Image Processing, 2015Conditional 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 +4 more
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Conditioned Simulations of Random Velocity Fields
Mathematical Geosciences, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Geraets, David +2 more
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Model fusion of conditional random fields
2007 IEEE International Conference on Systems, Man and Cybernetics, 2007This paper introduces two model fusion methods on a series of sub-models of Conditional Random Fields (CRFs): majority voting and feature fusion. The former performs on the results of each participant without any consideration about the underlying details of each sub-model, and the latter takes place on feature level to produce modified feature weights
Lu Li +3 more
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Variational inference for conditional random fields
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010Conditional 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
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Infinite Latent Conditional Random Fields
2013 IEEE International Conference on Computer Vision Workshops, 2013In this paper, we present Infinite Latent Conditional Random Fields (ILCRFs) that model the data through a mixture of CRFs generated from Dirichlet processes. Each CRF represents one possible explanation of the data. In addition to visible nodes and edges that exist in classic CRFs, it generatively models the distribution of different CRF structures ...
Yun Jiang, Ashutosh Saxena
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Distributed training for Conditional Random Fields
Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010), 2010This paper proposes a novel distributed training method of Conditional Random Fields (CRFs) by utilizing the clusters built from commodity computers. The method employs Message Passing Interface (MPI) to deal with large-scale data in two steps. Firstly, the entire training data is divided into several small pieces, each of which can be handled by one ...
Xiaojun Lin 0002 +3 more
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Conditional Random Fields for Intrusion Detection
21st International Conference on Advanced Information Networking and Applications Workshops (AINAW'07), 2007An 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 +2 more
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