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Size: 909312 |
Author: 萧峙清 |
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Description: 基于小波变换的特征检索算法，用了广义高斯函数和K-L距离为相似侧度 -Wavelet-Based Texture Retrieval Using Generalized Gaussian Density and Kullback–Leibler Distance
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Size: 22528 |
Author: 于梅 |
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Description: KLDIV Kullback-Leibler or Jensen-Shannon divergence between two distributions.
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Size: 2016256 |
Author: Jun-You Lin |
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Description: Kullback-leibler divergence
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Size: 2048 |
Author: kathie |
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Description: A package for wavelet-based texture retrieval: MATLAB source code that produced the results in the paper Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance.
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Size: 39936 |
Author: dao |
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Description: 3 books about distance function and Maximum Likelihood Estimation: Earth s Mover Distance, Kullback-Leibler, MLE
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Size: 1897472 |
Author: ChipChipKnight |
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Description: Joaquin Goñ i <jgoni@unav.es> &
Iñ igo Martincorena <imartincore@alumni.unav.es>
University of Navarra - Dpt. of Physics and Applied Mathematics &
Centre for Applied Medical Research. Pamplona (Spain).
December 13th, 2007. Information Theory Toolbox v1.0
The Toolbox includes:
-entropy
-conditional entropy
-mutual information
-redundancy
-symmetric uncertainty
-Kullback-Leibler divergence
-Jensen-Shannon divergence
type help for each command to get a detailed description
Citation:
If you use them for your academic research work,please kindly cite this
toolbox as:
Joaquin Goñ i, Iñ igo Martincorena. Information Theory Toolbox v1.0.
University of Navarra - Dpt. of Physics and Applied Mathematics &
Centre for Applied Medical Research. Pamplona (Spain).
-Joaquin Goñ i <jgoni@unav.es> &
Iñ igo Martincorena <imartincore@alumni.unav.es>
University of Navarra- Dpt. of Physics and Applied Mathematics &
Centre for Applied Medical Research. Pamplona (Spain).
December 13th, 2007. Information Theory Toolbox v1.0
The Toolbox includes:
-entropy
-conditional entropy
-mutual information
-redundancy
-symmetric uncertainty
-Kullback-Leibler divergence
-Jensen-Shannon divergence
type help for each command to get a detailed description
Citation:
If you use them for your academic research work,please kindly cite this
toolbox as:
Joaquin Goñ i, Iñ igo Martincorena. Information Theory Toolbox v1.0.
University of Navarra- Dpt. of Physics and Applied Mathematics &
Centre for Applied Medical Research. Pamplona (Spain).
Platform: |
Size: 16384 |
Author: le thanh tan |
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Description: This function calculates the Kullback Leibler divergence distance for Gaussians.
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Size: 1024 |
Author: ANU |
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Description: 自动谱分析：可用于丢失/采样/子束光谱分析；矢量自动迭代，可用于建模，故障诊断；-The applications of this additional toolbox are:
- Automatic spectral analysis for Irregular sampling/Missing data, analysis of spectral subbands,
- Vector Autoregressive modeling and Detection [uses ARMASA]
- Reduced statistics ARMAsel: A compact yet accurate ARMA model is obtained based on a given power spectrum. Can be used for generation of colored noise with a prescribed spectrum.
- ARfil algorithm: The analysis of missing data/irregularly sampled signals
- Subband analysis: Accurate analysis of a part of the power spectrum
- Detection: Generally applicable test statistic to determine whether two signals have been generated by the same process or not. Based on the Kullback-Leibler index or Likelihood Ratio.
- Analysis of segments of data, possibly of unequal length.
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Size: 302080 |
Author: 王佳 |
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Description: Structured covariance estimation. The routine takes a covariance matrix as input and returns the Toeplitz matrix that lies closest to it, in the sense that it minimizes the Kullback-Leibler divergence between the two. Input must be a real, square, symmetric and positive semi-definite matrix.
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Size: 1024 |
Author: ruso |
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Description: Kullback-Leibler or Jensen-Shannon divergence between two distributions.
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Size: 2048 |
Author: ysd |
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Description: 语音处理GMM相关算法,1.计算概率密度并画出高斯混合模型，2.计算边际，条件混合高斯密度,3估计两个GMM模型的Kullback-Leibler divergence。-GMM relating to speech processing algorithms.1,to calculate probability densities from or plot a Gaussian mixture model.2,marginal and conditional Gaussian mixture densities. 3, Kullback-Leibler divergence between two GMMs .
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Size: 21504 |
Author: 王愈 |
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Description: This is code to calculate Kullback-Leibler (KL) distance between histograms. It can apply for 2D image
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Size: 1024 |
Author: johnson |
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Description: 14的快速近似Kullback-Leibler距离依赖树和隐马尔科夫models.pdf-14Fast approximation of Kullback-Leibler distance for dependence trees and hidden Markov models.pdf
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Size: 102400 |
Author: fangms5 |
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Description: Kullback lieber clustering
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Size: 750592 |
Author: Osamah |
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Description: This function calculates the Kullback Leibler divergence distance for Gaussians.
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Size: 1024 |
Author: zhuia299517 |
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Description: BN网络参数之间的KL距离 (Kullback–Leibler Distance) 计算，用于比较相似度-BN KL distance between network parameters calculation, used to compare similarity
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Size: 1024 |
Author: west |
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Description: This paper presents a novel active contour model in a variational level set formulation for simultaneous segmentation and
bias field estimation of medical images. An energy function is formulated based on improved Kullback-Leibler distance (KLD)
with likelihood ratio. According to the additive model of images with intensity inhomogeneity, we characterize the statistics of
image intensities belonging to each different object in local regions as Gaussian distributions with different means and variances.
Then, we use the Gaussian distribution with bias field as a local region descriptor in level set formulation for segmentation(This paper presents a novel active contour model in a variational level set formulation for simultaneous segmentation and
bias field estimation of medical images. An energy function is formulated based on improved Kullback-Leibler distance (KLD)
with likelihood ratio. According to the additive model of images with intensity inhomogeneity, we characterize the statistics of
image intensities belonging to each different object in local regions as Gaussian distributions with different means and variances.
Then, we use the Gaussian distribution with bias field as a local region descriptor in level set formulation for segmentation and
bias field correction of the images with inhomogeneous intensities. Therefore, image segmentation and bias field estimation are
simultaneously achieved by minimizing the level set formulation)
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Size: 2441216 |
Author: song86
|
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