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Unsupervised Learning With Term Clustering For Thematic SegmentationOf Texts
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Clustering is the unsupervised classification of patterns (observations, data items,
or feature vectors) into groups (clusters). The clustering problem has been
addressed in many contexts and by researchers in many disciplines this reflects its
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Clustering is the unsupervised classification of patterns (observations, data items,
or feature vectors) into groups (clusters). The clustering problem has been
addressed in many contexts and by researchers in many disciplines this reflects its
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Clustering is the unsupervised classification of patterns (observations, data items,
or feature vectors) into groups (clusters). The clustering problem has been
addressed in many contexts and by researchers in many disciplines this reflects its
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聚类分析是多元统计分析的一种，也是无监督模式识别的一个重要分支，在模式分类 图像处理和模糊规则处理等众多领域中获得最广泛的应用。它把一个没有类别标记的样本按照某种准则划分为若干子集，使相似的样本尽可能归于一类，而把不相似的样本划分到不同的类中。硬聚类把每个待识别的对象严格的划分某类中，具有非此即彼的性质，而模糊聚类建立了样本对类别的不确定描述，更能客观的反应客观世界，从而成为聚类分析的主流。-Clustering analysis is a kind of multivariate statis
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BIRCH (balanced iterative reducing and clustering using hierarchies) is an unsupervised data mining algorithm used to perform hierarchical clustering over particularly large data-sets. An advantage of Birch is its ability to incrementally and dynamic
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BAMIC是一种multi-instance聚类的算法。-BAMIC is a package for multi-instance clustering. This package includes the MATLAB implementation of BAMIC, which is designed to deal with unsupervised multi-label learning problems. It is particularly useful when real-wo
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关于最新ap算法的开创性重要文章,应用数据分类-Affi nity propagation (AP) was recently introduced as an unsupervised
learning algorithm for exemplar-based clustering. We present a deriva-
tion of AP that is much simpler than the original one and is based on a
qu
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介绍模式识别的基本概念，详述了贝叶斯，参数估计，线性分类器，神经网络，随机方法，无监督学习与聚类等-Introduce the basic concepts of pattern recognition, Bayesian detailed, parameter estimation, linear classifiers, neural networks, stochastic methods, unsupervised learning and clustering, etc.
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无监督图像自动分类系统，我们在VC中做了一个基于聚类的图像分类方法，可以选择不同的图像特征，还有二个不同的显示方法。使用方法见“使用说明”。-Unsupervised image automatic classification system, we made a VC in the image classification method based on clustering, you can choose different image features, there are two dif
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Key Features
* Neural network design, training, and simulation
* Pattern recognition, clustering, and data-fitting tools
* Supervised networks including feedforward, radial basis, LVQ, time delay, nonlinear autoregressive (NARX), and laye
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利用K均值聚类实现非监督分类的功能，可以将大部分常用格式图像进行分类，程序功能强大。-K-means clustering function of unsupervised classification, can be the most common format for image classification, and powerful program.
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机器学习matlab源代码，包括多分类SVM，模式识别，特征选择，回归等算法。-The spider is intended to be a complete object orientated environment for machine learning in Matlab. Aside from easy use of base learning algorithms, algorithms can be plugged together and can be compared with
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1.Hopfield 學習演算法(功能:能辨識英文字母)
2.Competitive 競爭式學習演算法(功能:執行非監督式的資料分群)-1.Hopfield learning algorithm (function: to identify letters)
2.Competitive competitive learning algorithm (function: the implementation of unsupervised data clustering)
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关于变化检测的东西，很好!Fuzzy clustering algorithms for unsupervised change detection in remote sensing images-The Change Detection!Fuzzy clustering algorithms for unsupervised change detection in remote sensing images
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有监督SOM网络，克服了无监督SOM聚类不准确的问题-The supervised SOM network, to overcome the unsupervised SOM clustering inaccurate
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在聚类分析中，K-均值聚类算法（k-means algorithm）是无监督分类中的一种基本方法，其也称为C-均值算法，其基本思想是：通过迭代的方法，逐次更新各聚类中心的值，直至得到最好的聚类结果。
-In cluster analysis, K-means clustering algorithm (k-means algorithm) is unsupervised classification is a basic method, which is also known as C
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模糊聚类分析作为无监督机器学习的主要技术之一，是用模糊理论对重要数据分析和建模的方法，建立了样本类属的不确定性描述，能比较客观地反映现实世界，它已经有效地应用在大规模数据分析、数据挖掘、矢量量化、图像分割、模式识别等领域，具有重要的理论与实际应用价值，随着应用的深入发展，模糊聚类算法的研究不断丰富-Unsupervised fuzzy clustering analysis as the main machine learning techniques is the use of fuzzy t
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包括无监督和监督的机器学习技术
• K-means and other clustering tools
• Neural Networks
• Decision trees and ensemble learning
• Naï ve Bayes Classification
• Linear, logistic and nonlinear regression-Highlights include unsu
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K-均值聚类算法，属于无监督机器学习算法，发现给定数据集的k个簇的算法。
首先，随机确定k个初始点作为质心，然后将数据集中的每个点分配到一个簇中，为每个点找距其最近的质心，
将其分配给该质心对应的簇，更新每一个簇的质心，直到质心不在变化。
K-均值聚类算法一个优点是k是用户自定义的参数，用户并不知道是否好，与此同时，K-均值算法收敛但是聚类效果差，
由于算法收敛到了局部最小值，而非全局最小值。
K-均值聚类算法的一个变形是二分K-均值聚类算法，该算法首先将所有点作为一个簇，然
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