Description: 详细论述了分层狄利克雷模型，以及此模型在机器学习中的应用-Layered detail Dirichlet model, as well as this model in the application of machine learning Platform: |
Size: 223232 |
Author:chunxiao |
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Description: In this paper, we propose a Bayesian methodology for
receiver function analysis, a key tool in determining the deep structure
of the Earth’s crust.We exploit the assumption of sparsity for
receiver functions to develop a Bayesian deconvolution method as
an alternative to the widely used iterative deconvolution.We model
samples of a sparse signal as i.i.d. Student-t random variables.
Gibbs sampling and variational Bayes techniques are investigated
for our specific posterior inference problem. We used those techniques
within the expectation-maximization (EM) algorithm to
estimate our unknown model parameters. The superiority of the
Bayesian deconvolution is demonstrated by the experiments on
both simulated and real earthquake data. Platform: |
Size: 3350528 |
Author:张洋 |
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Description: Pattern recognition has its origins in engineering, whereas machine learning grew
out of computer science. However, these activities can be viewed as two facets of
the same field, and together they have undergone substantial development over the
past ten years. In particular, Bayesian methods have grown from a specialist niche to
become mainstream, while graphical models have emerged as a general framework
for describing and applying probabilistic models. Also, the practical applicability of
Bayesian methods has been greatly enhanced through the development of a range of
approximate inference algorithms such as variational Bayes and expectation propagation.
Similarly, new models based on kernels have had significant impact on both
algorithms and applications. Platform: |
Size: 4551680 |
Author:sas |
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Description: 机器学习(Machine Learning, ML)是一门多领域交叉学科，涉及概率论、统计学、逼近论、凸分析、算法复杂度理论等多门学科。专门研究计算机怎样模拟或实现人类的学习行为，以获取新的知识或技能，重新组织已有的知识结构使之不断改善自身的性能-Pattern recognition has its origins in engineering, whereas machine learning grew
out of computer science. However, these activities can be viewed as two facets of
the same field, and together they have undergone substantial development over the
past ten years. In particular, Bayesian methods have grown from a specialist niche to
become mainstream, while graphical models have emerged as a general framework
for describing and applying probabilistic models. Also, the practical applicability of
Bayesian methods has been greatly enhanced through the development of a range of
approximate inference algorithms such as variational Bayes and expectation propagation.
Similarly, new models based on kernels have had significant impact on both
algorithms and applications. Platform: |
Size: 7962624 |
Author:王以良 |
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Description: lda的matlab实现，lda is a Latent Dirichlet Allocation (Blei et al., 2001) package written both in MATLAB and C (command line interface).
This package provides only a standard variational Bayes estimation that was first proposed, but has a simple textual data format that is almost the same as SVMlight or TinySVM.
This package can be used as an aid to understand LDA, or simply as a regularized alternative to PLSI, which has a severe overfitting problem due to its maximum likelihood structure.
For advanced users who wish to benefit from the latest result, consider using npbayes or MPCA: though, they have data formats different from above.-lda is a Latent Dirichlet Allocation (Blei et al., 2001) package written both in MATLAB and C (command line interface).
This package provides only a standard variational Bayes estimation that was first proposed, but has a simple textual data format that is almost the same as SVMlight or TinySVM.
This package can be used as an aid to understand LDA, or simply as a regularized alternative to PLSI, which has a severe overfitting problem due to its maximum likelihood structure.
For advanced users who wish to benefit from the latest result, consider using npbayes or MPCA: though, they have data formats different from above. Platform: |
Size: 24576 |
Author:乌龟 |
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Description: 脉冲噪声背景下的联合稀疏恢复方法， 在不同背景下给出了测试结果-presents a robust solution for joint sparse
recovery (JSR) under impulsive noise. The unknown measurement
noise is endowed with the Student-t distribution, then a
novel Bayesian probabilistic model is proposed to describe the
JSR problem. To effectively recover the joint row sparse signal,
variational Bayes (VB) method is introduced for Bayesian theory
based JSR algorithms such that it overcomes the intractable
integrations inherent. Simulation results verify that the proposed
algorithm significantly outperforms the existing algorithms under
impulsive noise. Platform: |
Size: 1173504 |
Author:bigbigtom |
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Search - variational bayes