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machine-leaning-
- 机器学习的聚类代码,gmm、层次聚类、k-means,并包含各种数据文件-machine leaning code
MachineLearning
- 机器学习的十大算法,AdaBoost,Apriori,CART,EM,K-means,kNN,PageRank,SVM-Ten machine learning algorithms, AdaBoost, Apriori, CART, EM, K-means, kNN, PageRank, SVM
machine-learning-3
- 机器学习算法之EM与K-MEANS,经典的机器学习的外文资料,该资料描述详细,便于大家的学习。-The EM machine learning algorithms with K-MEANS, classical machine learning foreign language information, the information described in detail, easy to learn from everyone.
Machine_Learning
- 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
KMeans
- C++ 实现K-means,机器学习部分,附带测试数据-C++ realize K-means, Machine Learning
KMeans
- 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
kMeans
- 机器学习算法,无监督学习,利用k均值聚类算法对未标注数据分组-Machine learning algorithms, unsupervised learning, the use of k-means clustering algorithm for unlabeled data packets
matlab_kmeans
- k-means无监督机器学习聚类算法,使得同聚类中的记录欧式距离最小。-K-means machine learning method without instruction, minilizing the distances of records in each clusters the.
dlib-18.14.tar
- 机器学习的范畴,包括SVMs (based on libsvm), k-NN, random forests, decision trees。可以对任意的数据操作-Its focus is on supervised classification with several classifiers available: SVMs (based on libsvm), k-NN, random forests, decision trees. It also performs feature sel
Ch10
- 这是机器学习中k均值聚类的源码希望有需要的同学一起交流。-This is the exchange of machine learning in k-means clustering source code that are in need of the students.
Kmeans
- 《视觉机器学习20讲配套仿真代码》——1、K-means学习-" Vision Machine Learning Lecture 20 supporting the simulation code" 1, K-means learning
Kmeans
- 自己实现机器学习十大算法中的k均值算法,经过测试,算法运行很好-Own machine learning algorithm of the ten k-means algorithm, tested, the algorithm runs very well
Cluster
- 机器学习和数据挖掘中常用的K-means聚类算法,包含两个文件,kmeans.py是Python实现代码,bank-data.csv是测试数据-Machine learning and data mining commonly used K-means clustering algorithm contains two files, kmeans.py is a Python implementation code, bank-data.csv test data
ex5
- 机器学习 5、k - means聚类 开发环境Octave-Programming Exercise 5: K-means Clustering and Principal Component Analysis Machine Learning
bang-yh72
- 是机器学习的例程,基于K均值的PSO聚类算法,内含心电信号数据及运用MATLAB写的源代码。- Machine learning routines, K-means clustering algorithm based o
kmeans
- 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)
DBSCAN聚类
- Python密度聚类 最近在Science上的一篇基于密度的聚类算法《Clustering by fast search and find of density peaks》引起了大家的关注(在我的博文“论文中的机器学习算法——基于密度峰值的聚类算法”中也进行了中文的描述)。于是我就想了解下基于密度的聚类算法,熟悉下基于密度的聚类算法与基于距离的聚类算法,如K-Means算法之间的区别。 基于密度的聚类算法主要的目标是寻找被低密度区域分离的高密度区域。与基于距离的聚类算法不同的是,基