Results 231 to 240 of about 286,362 (268)

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
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Infinite Latent Conditional Random Fields

2013 IEEE International Conference on Computer Vision Workshops, 2013
In 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), 2010
This 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, Liang Zhao, Dianhai Yu, Xihong Wu   +3 more
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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
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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
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Staging tissues with conditional random fields

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011
We present a framework for identifying disease states by classifying cells in the pathological regions of tissues into different categories. We use conditional random fields (CRF) to incorporate characteristics of cells and their spatial distributions.
Jagath C. Rajapakse, Song Liu
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Learning Conditional Random Fields for Stereo

2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007
State-of-the-art stereo vision algorithms utilize color changes as important cues for object boundaries. Most methods impose heuristic restrictions or priors on disparities, for example by modulating local smoothness costs with intensity gradients. In this paper we seek to replace such heuristics with explicit probabilistic models of disparities and ...
Daniel Scharstein, Chris Pal
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Margin Losses for Training Conditional Random Fields

Journal of Mathematical Imaging and Vision, 2016
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ehsan Ahmadi, Zohreh Azimifar
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Learning Flexible Features for Conditional Random Fields

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008
Extending traditional models for discriminative labeling of structured data to include higher-order structure in the labels results in an undesirable exponential increase in model complexity. In this paper, we present a model that is capable of learning such structures using a random field of parameterized features.
Liam Stewart, Xuming He 0001, Richard S. Zemel   +2 more
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Neural conditional random fields

2010
We propose a non-linear graphical model for structured prediction. It combines the power of deep neural networks to extract high level features with the graphical framework of Markov networks, yielding a powerful and scalable probabilistic model that we apply to signal labeling tasks.
Do, Trinh Minh Tri, Artières, Thierry
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