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研究马尔科夫随机场和吉布斯随机场理论之间关系的文章，有严格的推理。希望对大家有用-Of Markov random fields and Gibbs random field theory of the relationship between the articles, there are strict reasoning. Hope to useful
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This a reference implementation for the synthetic experiments on lower
linear envelope inference and learning described in
"Max-margin Learning for Lower Linear Envelope Potentials in Binary
Markov Random Fields", Stephen Gould, ICML 2011
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libDAI is a free/open source C++ library that provides implementations of
various (approximate) inference methods for discrete graphical models. libDAI
supports arbitrary factor graphs with discrete variables this includes
discrete Markov Rando
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Pixon-based Image Segmentation with Markov random fields
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基于马尔科夫随机场的，例子学习超分辨率复原代码。-This is an implementation of the example-based super-resolution algorithm. Although the applications of MSFs have now extended beyond example-based super resolution and texture synthesis, it is still of great value to revisit
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MRF图像分割，通过Markov随机场（MRF）实现图像的分割-MRF image segmentation, image segmentation by Markov random fields (MRF)
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基于马尔科夫随机场的SAR图像分割方法，对应于采用最大后验概率准则对SAR目标切片图像分割，采用聚类分析算法求解。-SAR image segmentation based on Markov random fields, which corresponds to using the maximum posterior probability criteria for SAR Target Chip Image segmentation using cluster analysis algori
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图像去噪-A Generative Perspective on MRFs in Low-Level Vision-A Generative Perspective on MRFs in Low-Level Vision
Markov random fields (MRFs) are popular and generic
probabilistic models of prior knowledge in low-level vision.
Yet their generative
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应用于移动传感器网络的高效贝叶斯空间预测方案-Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields
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This directory contains the MRF energy minimization software accompanying the paper
[1] A Comparative Study of Energy Minimization Methods for Markov Random Fields.
R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, V. Kolmogorov,
A. Agarw
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针对各种机器学习，深度学习领域的一个matlab工具包-A machine learning library focused on deep learning.Following algorithms and models are provided along with some static utility classes:
- Naive Bayes, Linear Regression, Logistic Regression, Softmax Regression, Linear S
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高斯马尔科夫随机场的理论模型，主要介绍应用，很详细，欢迎大家来学习。-High 斯马尔科夫 airport with theoretical models, mainly on applications, in great detail, welcome everyone to learn.
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