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Description: 已有的聚类集算法基本上都是非监督聚类集成算法,这样不能利用已知信息,使得聚类集成的准确性、鲁棒性和稳定性降低.把半监督学习和聚类集成结合起来,设计半监督聚类集成模型来克服这些缺点.主要工作包括:第一,设计了基于贝叶斯网络的半监督聚类集成(semi-supervised cluster ensemble,简称SCE)模型,并对模型用变分法进行了推理求解;第二,在此基础上,给出了EM(expectation maximization)框架下的具体算法;第三,从UCI(University of California, Irvine)机器学习库中选取部分数据来做实验.实验结果表明,SCE模型本身及其变分推理后所设计的EM算法都能进行半监督聚类集成,总的来说,效果比NMFS(algorithm of nonnegative-matrix-factorization based semi-supervised)、半监督SVM(support vector machine)、LVCE(latent variable model for cluster ensemble)等算法要好.该半监督聚类集成模型聚集了半监督学习和聚类集成两者的优点,最后的聚类结果比单纯的半监督聚类或聚类集成的效果都要好.
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Size: 698421 |
Author: imgump@qq.com |
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Description: 数据挖掘技术EM算法的matlab程序代码，还会有更多哟！-data mining technology EM algorithm Matlab code, there will be even more yo!
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Size: 5120 |
Author: 杨爱萍 |
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Description: 实现了从wave中读取数据，并通过mfcc提取特征系数。再通过分裂法聚类，最后使用EM算法建立GMM。-Realize the data read from the wave, and through MFCC feature extraction coefficient. Clustering through secession law, and finally the use of EM algorithm for the establishment of GMM.
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Size: 5120 |
Author: gujunjun |
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Description: 这是小弟在实验室做的说话人身份识别程序，采用MFCC参数提取，并建立GMM模型来进行识别。希望会对大家有帮助。-This is the boy in the laboratory to do the speaker identification procedure for the extraction of MFCC parameters and to establish GMM model to be identified. I hope everyone will help.
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Size: 380928 |
Author: feng |
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Description: 统计模式识别算法包，包括线性分类算法，SVM，PCA，LDA，EM，k-means分类等多种常用的模式识别算法。-Statistical pattern recognition algorithm package, including a linear classification algorithm, SVM, PCA, LDA, EM, k-means classification and many other commonly used pattern recognition algorithms.
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Size: 5998592 |
Author: Xiaoyue Jiang |
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Description: SVM(支持向量机）和EM（最大熵）文本分类算法，压缩包中包括了测试文本（环境类和计算机类），词典，停用词表等。-SVM (support vector machine) and EM (maximum entropy) text classification algorithm, compressed package includes test text (environmental and computer), dictionary, thesaurus, such as disabled.
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Size: 2591744 |
Author: frank |
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Description: 统计模式识别工具包(统计模式识别工具包，包含EM，SVM等方法。)-Statistical Pattern Recognition Kit (statistical pattern recognition tool kit, including EM, SVM and other methods.)
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Size: 4090880 |
Author: lyli |
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Description: 使用EM算法模拟高斯混合模型（GMM）的构成-Analog EM algorithm using Gaussian mixture model (GMM) the composition of
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Size: 1024 |
Author: qjc |
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Description: 机器学习是人工智能的一个子领域，是人工智能中非常活跃且范围甚广的主要核心研究领域之一，也是现代智能系统的关键环节和瓶颈。机器学习吸取了人工智能、概率统计、计算复杂性理论、控制论、信息论、哲学、生理学、神经生物学等学科的成果，主要关注于开发一些让计算机可以自动学习的技术，并通过经验提高系统自身的性能。本文介绍了机器学习的概念、基本结构和发展，以及各种机器学习方法，包括机械学习、归纳学习、类比学习、解释学习、基于神经网络的学习以及知识发现等，并简单叙述了机器学习的相关算法，包括决策树算法、随机森林算法、人工神经网络算法、SVM算法、Boosting与Bagging算法、关联规则算法、贝叶斯学习算法以及EM算法等，最后还指出了机器学习的应用及其发展趋势。-Machine learning is a subset of the field of Artificial Intelligence,is very active in Artificial Intelligence and a wide range of research in the field of one of the main core, is the key and the bottleneck of a modern intelligent system.Machine Learning has absorbed the results of Artificial Intelligence, Probability and Statistics, Computational Complexity Theory, Cybernetics, Information Theory, Philosophy, Physiology and Neurobiology, and other disciplines.It concerns mainly about how to develop some of technology which can make the computer auto-learning,and through experiences to improve the performance of the system itself.This paper introduces the concept,the basic structure and development of machine learning, and a variety of machine learning methods,including machine learning, inductive learning, learning by analogy, explanation-based learning,based on neural network learning and knowledge discovery, and so on. And briefly descrip the algorithms of machine learning,includin
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Author: lzl |
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Description: This paper presents the top 10 data mining algorithms identified by the IEEE
International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM,
Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms
are among the most influential data mining algorithms in the research community.With each
algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and
reviewcurrent and further research on the algorithm. These 10 algorithms cover classification,
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Size: 622592 |
Author: sukmawati |
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Description: This paper presents the top 10 data mining algorithms identified by the IEEE
International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM,
Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms
are among the most influential data mining algorithms in the research community.With each
algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and
reviewcurrent and further research on the algorithm. These 10 algorithms cover classification
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Size: 635904 |
Author: ParisM |
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Description: 模式识别工具箱函数，包含多种模式识别方法如KNN，PCA，SVM,C4.5,EM算法-Pattern recognition toolbox functions, including a variety of pattern recognition methods such as KNN, PCA, SVM, C4.5, EM algorithm, etc.
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Size: 362496 |
Author: Arthur |
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Description: stprtool的使用文档，非常好的模式识别工具包，里面有关于核的以及贝叶斯的相关程序，以及PCA，EM，SVM等方法。 -stprtool the use of the document, very good pattern recognition tool kit, which has nuclear as well as Bayes procedures, as well as PCA, EM, SVM and other methods.
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Size: 923648 |
Author: wangpw |
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Description: 北航计算机学院研究生机器学习课程讲义，涵盖了机器学习领域大部分内容，如SVM、EM、boosting等。深入浅出，易于自学-Beihang University Graduate School of Computer Science Machine Learning Lecture Notes, covering most of machine learning, such as SVM, EM, boosting and so on. Easy to understand and self-learn
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Author: Carter |
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Description: （经典聚类算法）
国际权威的学术组织the IEEE International Conference on Data Mining (ICDM) 2006年12月评选出了数据挖掘领域的十大经典算法：C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.
不仅仅是选中的十大算法，其实参加评选的18种算法，实际上随便拿出一种来都可以称得上是经典算法，它们在数据挖掘领域都产生了极为深远的影响。-(Classical clustering algorithm)
International authoritative academic organization of the IEEE International Conference on Data Mining (ICDM) in December 2006 selected the top ten of the field of data mining algorithm: the C4.5, k-Means, SVM, of Apriori, the EM, the PageRank, AdaBoost, kNN , the Naive Bayes, and the CART.
Not just the selected 10 algorithms, in fact, participate in the selection of 18 kinds of algorithms, in fact, easily come up with one can be called a classical algorithm in the field of data mining, they have had far-reaching impact.
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Size: 3922944 |
Author: 赵鑫维 |
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Description: 讲述了最著名的十大数据挖掘算法，经典资料，国际权威的学术组织the IEEE International Conference on Data Mining (ICDM) 2006年12月评选出了数据挖掘领域的十大经典算法：C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.-About the top ten most famous data mining algorithms, the classical information, the international authority of the academic organization of the IEEE International Conference on Data Mining (ICDM) 2006, selected the top ten of the field of data mining algorithms: the C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.
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Size: 57344 |
Author: 吴贵锋 |
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Description: 介绍数据挖掘的10种主要算法及其应用 一种透过数理模式来分析企业内储存的大量资料，以找出不同的客户或市场划分，分析出消费者喜好和行为的方法。 -Top 10 algorithms in data mining his paper presents the top 10 data mining algorithms identified by the IEEE
International Conference on Data Mining (ICDM) in December 2006: C4.5,k-Means, SVM,
Apriori, EM, PageRank, AdaBoost,kNN, Naive Bayes, and CART. These top 10 algorithms
are among the most influential data mining algorithms in the research community. With each
algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and
review current and further research on the algorithm. These 10 algorithms cover classification,
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Size: 633856 |
Author: andyzygg |
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Description: 模式识别课程作业，em算法，svm算法，适合初学者，绝对简单例子-Pattern Recognition course work, em algorithm, svm algorithm, suitable for beginners, absolutely simple example
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Size: 189440 |
Author: 吴照 |
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Description: 机器学习十大算法之一：EM算法。能评得上十大之一，让人听起来觉得挺NB的。什么是NB啊，我们一般说某个人很NB，是因为他能解决一些别人解决不了的问题。神为什么是神，因为神能做很多人做不了的事。那么EM算法能解决什么问题呢？或者说EM算法是因为什么而来到这个世界上，还吸引了那么多世人的目光-Expectation Maximization Algorithm
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Size: 424960 |
Author: 991621 |
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Description: 对机器学习领域的十个经典算法进行了详细介绍，包括：AdaBoost、Apriori、C4.5、CART、EM、K-means、kNN、PageRand、SVM和朴素贝叶斯(Ten classical algorithms in machine learning domain are introduced in detail, including AdaBoost, Apriori, C4.5, CART, EM, K-means, kNN, PageRand, SVM and Nave Bayes)
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Author: kmsj
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