Description: 经典的最大似然法分类法的C语言实现，有助于深入了解遥感分类原理。-This program implements the maximum likelihood classification procedure. ouput:1.classified image, and 2. probability file.
Note: For constructong variance-covariance matrix must be generic binary file.
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Size: 4096 |
Author:李会利 |
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Description: This function calculate the performance, based on Bayes theorem, of a clinical test. The input is based on a 2x2 matrix (true positive, false positives false negative, true negatives).
The Outputs are:
- Prevalence of disease
- Test Sensibility with 95 confidence interval
- Test Specificity with 95 confidence interval
- False positive and negative proportions
- Youden s Index
- Matthews Correlation Coefficient
- Number needed to Diagnose (NDD)
- Discriminant Power
- Test Accuracy
- Mis-classification Rate
- Positive predictivity
- Negative predictivity
- Positive Likelihood Ratio
- Negative Likelihood Ratio
- Test bias
- Diagnostic odds ratio
- Error odds ratio-This function calculate the performance, based on Bayes theorem, of a clinical test. The input is based on a 2x2 matrix (true positive, false positives false negative, true negatives).
The Outputs are:
- Prevalence of disease
- Test Sensibility with 95 confidence interval
- Test Specificity with 95 confidence interval
- False positive and negative proportions
- Youden s Index
- Matthews Correlation Coefficient
- Number needed to Diagnose (NDD)
- Discriminant Power
- Test Accuracy
- Mis-classification Rate
- Positive predictivity
- Negative predictivity
- Positive Likelihood Ratio
- Negative Likelihood Ratio
- Test bias
- Diagnostic odds ratio
- Error odds ratio Platform: |
Size: 3072 |
Author:Rafal |
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Description: Algorithms for QAM Signal Classification Using Maximum Likelihood Approach Based on the Joint Probability Densities of Phases and amplitudes Platform: |
Size: 212992 |
Author:HASHEM |
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Description: 《Software for Flexible Bayesian Modeling and Markov Chain Sampling》是机器学习领域专家Neal编写的用于Bayesian和马尔可夫链Linux下的C语言工具包。很有名，也很权威。
-This software supports Bayesian regression and classification models based on neural networks and Gaussian processes, and Bayesian density estimation and clustering using mixture models and Dirichlet diffusion trees. It also supports a variety of Markov chain sampling methods, which may be applied to distributions specified by simple formulas, including simple Bayesian models defined by formulas for the prior and likelihood. Platform: |
Size: 974848 |
Author:王磊 |
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Description: 针对MPSK信号的调制识别问题，本文提出了一种新的最大似然比调制分类算法，采用平均释然比检测方法，通过对接收信号的的离散化和对主要参数的简要近似，得到了离散情况下的近似分类统计量！-MPSK signals for modulation recognition problem, we propose a new maximum likelihood modulation classification algorithm, using an average of more than relieved detection method, the received signal by the discrete and approximate summary of the main parameters obtained discrete case statistics under the approximate classification! Platform: |
Size: 457728 |
Author:李白 |
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Description: 人脸识别的稀疏表示识别方法将稀疏表示的保真度表示为余项的L2范数，但最大似然估计理论证明这样的假设要求余项服从高斯分布，实际中这样的分布可能并不成立，特别是当测试图像中存在噪声、遮挡和伪装等异常像素，这就导致传统的保真度表达式所构造的稀疏表示模型对上述这些情况缺少足够的鲁棒性。而最大似然稀疏表示识别模型则基于最大似然估计理论，将保真度表达式改写为余项的最大似然分布函数，并将最大似然问题转化为一个加权优化问题-Recently the sparse representation (or coding) based classification (SRC) has been successfully used in face recognition. In SRC, the testing image is represented as a sparse linear combination of the training samples, and the representation fidelity is measured by the 𝑙 2-norm or 𝑙 1-norm of coding residual. Such a sparse coding model actually assumes that the coding residual follows Gaussian or Laplacian distribution, which may not be accurate enough to describe the coding errors in practice. In this paper, we propose a new scheme, namely the robust sparse coding (RSC), by modeling the sparse coding as a sparsityconstrained
robust regression problem. The RSC seeks for the MLE (maximum likelihood estimation) solution of the
sparse coding problem, and it is much more robust to outliers (e.g., occlusions, corruptions, etc.) than SRC. An efficient iteratively reweighted sparse coding algorithm is
proposed to solve the RSC model. Platform: |
Size: 18704384 |
Author:徐波 |
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Description: 不错的GM_EM代码。用于聚类分析等方面。- GM_EM- fit a Gaussian mixture model to N points located in n-dimensional
space.
Note: This function requires the Statistical Toolbox and, if you wish to
plot (for k = 2), the function error_ellipse
Elementary usage:
GM_EM(X,k)- fit a GMM to X, where X is N x n and k is the number of
clusters. Algorithm follows steps outlined in Bishop
(2009) Pattern Recognition and Machine Learning , Chapter 9.
Additional inputs:
bn_noise- allow for uniform background noise term ( T or F ,
default T ). If T , relevant classification uses the
(k+1)th cluster
reps- number of repetitions with different initial conditions
(default = 10). Note: only the best fit (in a likelihood sense) is
returned.
max_iters- maximum iteration number for EM algorithm (default = 100)
tol- tolerance value (default = 0.01)
Outputs
idx- classification/labelling of data in X
mu- GM centres Platform: |
Size: 3072 |
Author:朱魏 |
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Description: 利用男女身高体重利用贝叶斯算法，最大似然估计，最小风险估计等进行性别分类判决-The use of male and female height and weight using Bayesian algorithm, maximum likelihood estimation, minimum risk estimation for gender classification ruling Platform: |
Size: 4096 |
Author:周 |
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Description: 经典的最大似然法分类法的C语言实现，有助于深入了解遥感分类原理。-This program implements the maximum likelihood classification procedure. ouput:1.classified image, and 2. probability file.Note: For constructong variance-covariance matrix must be generic binary file. Platform: |
Size: 3072 |
Author:whicme |
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Description: 基于MATLAB的模式识别的参数估计的最大似然估计，处理水环境分类问题，共分为5类，最后利用贝叶斯决策得出五类总的识别率。
-Maximum likelihood estimation of parameters of pattern recognition of MATLAB estimation based on classification of water environment treatment problem, is divided into 5 categories, the last use of Bayesian decision obtained five types of the total recognition rate.
Platform: |
Size: 47104 |
Author:张文波 |
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Description: 实现简单的遥感图像分类处理,使用的语言为C++，其中监督分类为最大似然算法，非监督分类为ISODATA算法-Can achieve a simple remote sensing image classification, the language used is C++, which is the maximum likelihood supervised classification algorithms, unsupervised classification algorithm for ISODATA Platform: |
Size: 227328 |
Author:lizhipeng |
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Description: In this paper we present Discriminative Random Fields (DRF), a discrim- inative framework for the classification of natural image regions by incor- porating neighborhood spatial dependencies in the labels as well as the observed data. The proposed model exploits local discriminative models and allows to relax the assumption of conditional independence of the observed data given the labels, commonly used in the Markov Random Field (MRF) framework. The parameters of the DRF model are learned using penalized maximum pseudo-likelihood method. Furthermore, the form of the DRF model allows the MAP inference for binary classifica- tion problems using the graph min-cut algorithms. The performance of the model was verified on the synthetic as well as the real-world images. The DRF model outperforms the MRF model in the experiments.
Platform: |
Size: 151552 |
Author:asdf12341234 |
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Description: Abstract—In slow frequency hopping systems, it is critical
to detect, classify, and mitigate interference, as it degrades the
system performance. Generalized likelihood ratio test algorithm
was proposed for the detection of interference. However, it could
not classify the type of interference. In this paper, we propose
algorithms to classify between follower/stationary interference
and multitone interference/partial-band noise interference. Classification
of interference in various types of modulation schemes
is performed based on a ratio-testing approach, where the ratio
is compared with a predetermined threshold value. It is critical
to set the threshold at an optimal value in order to achieve a good
classification performance. The optimal value and classification
performance are obtained through simulation results. Platform: |
Size: 139264 |
Author:ppdghius |
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Description: 用于高光谱图像处理的matlab程序，包括图像融合，降维和图像的极大似然分类程序(Matlab program for hyperspectral image processing, including image fusion, dimension reduction, and maximum likelihood classification of images) Platform: |
Size: 3072 |
Author:萌萌66 |
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