Description: Spectral Python (SPy) is a python package for reading, viewing, manipulating, and classifying hyperspectral image (HSI) data. SPy includes functions for clustering, dimensionality reduction, supervised classification, and more. Platform: |
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Author:john |
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Description: python语言编写的一个Cure算法，可实现数据的分类-A cure algorithm programmed in python which an realize data clustering Platform: |
Size: 10240 |
Author:Lee |
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Description: 机器学习中文本聚类算法，里面有5个文件，包含Python实现代码和测试数据。-The clustering algorithm machine learning Chinese, there are five files that contain Python implementation code and test data. Platform: |
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Author:wangwendao |
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Description: 《机器学习实战》通过精心编排的实例，切入日常工作任务，摒弃学术化语言，利用高效可复用的Python代码阐释如何处理统计数据，进行数据分析及可视化。读者可从中学到一些核心的机器学习算法，并将其运用于某些策略性任务中，如分类、预测及推荐等。(As you work through the numerous examples, you'll explore key topics like classification, numeric prediction, and clustering. Along the way, you'll be introduced to important established algorithms, such as Apriori, through which you identify association patterns in large datasets and Adaboost, a meta-algorithm that can increase the efficiency of many machine learning tasks.) Platform: |
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Author:bfy
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Description: 对数据和图像进行聚类分析，k-means聚类方法多应用于模式识别，人工智能，机器学习等方面(Clustering analysis of data and images, K-means clustering method should be used in pattern recognition, artificial intelligence, machine learning and so on) Platform: |
Size: 1024 |
Author:Source Pill
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Description: Kernel Entropy Component Analysis,KECA方法的作者R. Jenssen自己写的MATLAB代码，文章发表在2010年5月的IEEE TPAMI上面-Kernel Entropy Component Analysis, by R. Jenssen, published in IEEE TPAMI 2010.(We introduce kernel entropy component analysis (kernel ECA) as a new method for data transformation and dimensionality reduction. Kernel ECA reveals structure relating to the Renyi entropy of the input space data set, estimated via a kernel matrix using Parzen windowing. This is achieved by projections onto a subset of entropy preserving kernel principal component analysis (kernel PCA) axes. This subset does not need, in general, to correspond to the top eigenvalues of the kernel matrix, in contrast to the dimensionality reduction using kernel PCA. We show that kernel ECA may produce strikingly different transformed data sets compared to kernel PCA, with a distinct angle-based structure. A new spectral clustering algorithm utilizing this structure is developed with positive results. Furthermore, kernel ECA is shown to be an useful alternative for pattern denoising.) Platform: |
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Author:daxingxing001
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Description: 中文文本处理的python程序，包括分词、删除特殊字符、删除停用词、爬虫程序、PCA降维、Kmean聚类、可视化等(Python programs for Chinese text processing, including participle, deleting special characters, deleting disuse words, crawler programs, PCA dimensionality reduction, Kmean clustering, visualization and so on) Platform: |
Size: 12288 |
Author:hello_spring |
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Description: 基于Pythoon的数值聚类分类算法，基于Python的三维立体点的空间最近邻分类(This example shows the effect of imposing a connectivity graph to capture local structure in the data. The graph is simply the graph of 20 nearest neighbors.
Two consequences of imposing a connectivity can be seen. First clustering
with a connectivity matrix is much faster.
Second, when using a connectivity matrix, average and complete linkage are
unstable and tend to create a few clusters that grow very quickly. Indeed,
average and complete linkage fight this percolation behavior by considering all the distances between two clusters when merging them. The connectivity graph breaks this mechanism. This effect is more pronounced for very
sparse graphs (try decreasing the number of neighbors in
kneighbors_graph) and with complete linkage. In particular, having a very small number of neighbors in the graph, imposes a geometry that is close to that of single linkage, which is well known to have this
percolation instability.) Platform: |
Size: 10240 |
Author:Merichiee |
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Description: 一个简单的模糊聚类的实例，实现了模糊聚类的基本原理，代码通俗易懂，带有数据集，可以直接运行(A simple example of fuzzy clustering, fuzzy clustering to achieve the basic principles of code easy to understand, with a data set, you can run directly) Platform: |
Size: 3072 |
Author:yuyinghua |
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Description: 本书介绍Python的计算机视觉编程，用清晰的Python示例，细致讲解对象识别、3D重建、立体图像、增强现实及其他计算机视觉应用技巧，给出了分析图像的工具与方法。内容：机器人导航、医学图像分析；图像映射与变换；多图像的3D重建；用聚类方法基于相似性和内容组织图像；基于视觉内容的图像检索技巧；实现图像内容分类和对象识别的算法；通过Python接口访问常用的OpenCV库。(This book introduces Python's computer vision programming. With clear Python examples, it elaborates on object recognition, 3D reconstruction, stereo image, augmented reality and other computer vision application techniques, and gives tools and methods for image analysis. Contents: Robot navigation, medical image analysis; image mapping and transformation; multi-image 3D reconstruction; image organization based on similarity and content using clustering method; image retrieval techniques based on visual content; algorithm for image content classification and object recognition; access to common OpenCV library through Python interface.) Platform: |
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Author:qhxinshou |
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