Description: This leading textbook provides a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. This is the first machine learning textbook to include a comprehensive coverage of recent developments such as probabilistic graphical models and deterministic inference methods, and to emphasize a modern Bayesian perspective. It is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. This hard cover book has 738 pages in full colour, and there are 431 graded exercises (with solutions available below). Extensive support is provided for course instructors. Platform: |
Size: 7775130 |
Author:arcanesky |
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Description: 本书介绍了一些常用的图论模型以及图论在通信，统计，优化，图像处理，机器学习等方面的应用-The formalism of probabilistic graphical models provides a unifying
framework for capturing complex dependencies among random
variables, and building large-scale multivariate statistical models.
Graphical models have become a focus of research in many statistical,
computational and mathematical fields, including bioinformatics,
communication theory, statistical physics, combinatorial optimization,
signal and image processing, information retrieval and statistical
machine learning. Many problems that arise in specific instances —
including the key problems of computing marginals and modes of
probability distributions — are best studied in the general setting.
Working with exponential family representations, and exploiting the
conjugate duality between the cumulant function and the entropy
for exponential families, we develop general variational representations
of the problems of computing likelihoods, marginal probabilities
and most probable configurations. We describe how Platform: |
Size: 1815552 |
Author:万毅 |
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Description: 贝叶斯网络是一种概率网络，它是基于概率推理的图形化网络，而贝叶斯公式则是这个概率网络的基础。贝叶斯网络是基于概率推理的数学模型,所谓概率推理就是通过一些变量的信息来获取其他的概率信息的过程，基于概率推理的贝叶斯网络(Bayesian network)是为了解决不定性和不完整性问题而提出的，它对于解决复杂设备不确定性和关联性引起的故障有很的优势，在多个领域中获得广泛应用。本算法用于weka算法包的拓展。-Bayesian network is a probabilistic network, which is based on graphical probability reasoning network, and Bayesian formula is the basis of the probability of the network. Bayesian network is based on the reasoning of the mathematical model of probability, the so-called probabilistic reasoning through a number of variables to obtain the probability of other information on the process of reasoning based on probabilistic Bayesian network (Bayesian network) is to address uncertainty and incomplete issues raised by it to address the complexity of equipment and the relevance of the uncertainty caused by the fault there are advantages in a number of broad areas of application. Weka algorithm of the algorithm for the expansion pack. Platform: |
Size: 8192 |
Author:zhangrui |
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Description: 一本将基于AP传播算法的半监督聚类的算方法书.对于聚类研究的很有帮助-The Uncapacitated Facility Location Prob-
lem (UFLP) is one of the most widely stud-
ied discrete location problems, whose appli-
cations arise in a variety of settings. We
tackle the UFLP using probabilistic infer-
ence in a graphical model- an approach that
has received little attention in the past. We
show that the fi xed points of max-product
linear programming (MPLP), a convexifi ed
version of the max-product algorithm, can
be used to construct a solution with a 3-
approximation guarantee for metric UFLP
instances. In addition, we characterize some
scenarios under which the MPLP solution is
guaranteed to be globally optimal. We eval-
uate the performance of both max-sum and
MPLP empirically on metric and non-metric
problems, demonstrating the advantages of
the 3-approximation construction and algo-
rithm applicability to non-metric instances. Platform: |
Size: 250880 |
Author:fanhaixiong |
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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: 一个中科院的老师的关于概率图模型的讲座。介绍的内容比较新。-A Chinese Academy of Sciences of the teacher s lectures on the probabilistic graphical model. Described is relatively new. Platform: |
Size: 952320 |
Author:梁运棠 |
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Description: Title: "Modeling Lung Cancer Diagnosis Using Bayesian Network Inference"
This demo illustrates a simple Bayesian Network example for exact probabilistic inference using Pearl s message-passing algorithm.
Introduction:
Bayesian networks (or belief networks) are probabilistic graphical models representing a set of variables and their dependencies.
The graphical nature of Bayesian networks and the ability of describing uncertainty of complex relationships in a compact manner provide a method
for modelling almost any type of data. Platform: |
Size: 80896 |
Author:atish |
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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: 该工具包囊括了无向图模型的一些常用算法，包括随机场模型，预测算法，解码算法和采样算法，非常实用。-UGM is a set of Matlab functions implementing various tasks in probabilistic undirected graphical models of discrete data with pairwise (and unary) potentials。 Platform: |
Size: 478208 |
Author:john |
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Description: PGM-Programming_Assignment_2,概率图模型的网络公开课的第二次作业的代码matlab版本-Code matlab version PGM-Programming_Assignment_2, open class of probabilistic graphical network model' s second job Platform: |
Size: 1197056 |
Author:qichengzuo |
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Description: 巨著《概率图模型》的英文原版，非常棒的一本书籍！相信搞人工智能的都会知道这部巨著- The great probability map model English original, very good book! I believe in artificial intelligence will know this masterpiece Platform: |
Size: 7524352 |
Author:徐俊俊 |
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Description: 很经典的一本书 主要讲述概率图模型的建模 参数化 和求解 适合研究机器学习的研究人员使用-Parametric modeling and solving classic book focuses on probabilistic graphical model suitable for the study researchers used machine learning Platform: |
Size: 25316352 |
Author:fk |
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Description: Probabilistic graphical models are now widely accepted as a powerful and
mature technology for reasoning under uncertainty, and there are many efficient algorithms for both inference
and learning available in open-source and commercial software. Platform: |
Size: 5819392 |
Author:tianwhuyh |
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Description: 贝叶斯网络是一种概率网络，它是基于概率推理的图形化网络，而贝叶斯公式则是这个概率网络的基础。贝叶斯网络是基于概率推理的数学模型，所谓概率推理就是通过一些变量的信息来获取其他的概率信息的过程，基于概率推理的贝叶斯网络(Bayesian network)是为了解决不定性和不完整性问题而提出的，它对于解决复杂设备不确定性和关联性引起的故障有很大的优势，在多个领域中获得广泛应用。(Bias network is a probabilistic network, which is a graphical network based on probabilistic reasoning, and the Bias formula is the basis of this probabilistic network. Bias network is a mathematical model based on probability theory, a process called probabilistic reasoning is through some variable information to obtain the probability information of other Bias network based on probabilistic inference (Bayesian network) is put forward in order to solve the uncertainty and incompleteness problem, it has great advantage for solving fault caused by complex equipment the uncertainty and relevance, is widely used in many fields.) Platform: |
Size: 3511296 |
Author:sugll
|
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