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Triangular-Chain Conditional Random Fields
IEEE Transactions on Audio, Speech, and Language Processing, 2008Sequential 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, 2011We 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, 2007State-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, 2016zbMATH 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, 2008Extending 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 +2 more
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Neural conditional random fields
2010We 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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Multi-Illuminant Estimation With Conditional Random Fields
IEEE Transactions on Image Processing, 2014Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a conditional random field over
Shida Beigpour +3 more
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Kernel conditional random fields
Twenty-first international conference on Machine learning - ICML '04, 2004Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models is given which shows how kernel conditional random fields arise from risk minimization procedures defined using Mercer kernels on labeled graphs.
John D. Lafferty +2 more
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Neural Gaussian Conditional Random Fields
2014We propose a Conditional Random Field (CRF) model for structured regression. By constraining the feature functions as quadratic functions of outputs, the model can be conveniently represented in a Gaussian canonical form. We improved the representational power of the resulting Gaussian CRF (GCRF) model by (1) introducing an adaptive feature function ...
Vladan Radosavljevic +2 more
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Conditioned Stochastic Processes for Conditional Random Fields
Journal of Engineering Mechanics, 1994Analytical development is presented for the theory of conditional random fields involving conditioning deterministic time functions. After discussion of their basic concept and their engineering significance, the probability distribution of the Fourier coefficients for conditioned stochastic processes is derived.
Kameda, H., Morikawa, H.
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