data mining k nearest neighbour
标签： neighbour nearest mining data
上传时间： 2017-09-15
上传用户：talenthn
Rainbow is a C program that performs document classification usingone of several different methods, including naive Bayes, TFIDF/Rocchio,K-Nearest neighbor, Maximum Entropy, Support Vector Machines, Fuhr sProbabilitistic Indexing, and a simple-minded form a shrinkage withnaive Bayes.
标签： classification different document performs
上传时间： 2015-03-03
上传用户：希酱大魔王
在visual basic环境下，实现K-Nearest neighbor算法。
上传时间： 2013-12-08
上传用户：ma1301115706
KNN算法的实现，K-Nearest neighbors聚类算法的matlab 实现
上传时间： 2013-12-19
上传用户：AbuGe
朴素贝叶斯(Naive Bayes, NB)算法是机器学习领域中常用的一种基于概率的分类算法，非常简单有效。k近邻法(K-Nearest Neighbor, kNN)[30,31]又称为基于实例(Example-based, Instance-bases)的算法，其基本思想相当直观：Rocchio法来源于信息检索系统，后来最早由Hull在1994年应用于分类[74]，从那以后，Rocchio方法就在文本分类中广泛应用起来。
上传时间： 2014-01-03
上传用户：wxhwjf
* acousticfeatures.m: Matlab script to generate training and testing files from event timeseries. * afm_mlpatterngen.m: Matlab script to extract feature information from acoustic event timeseries. * extractevents.m: Matlab script to extract event timeseries using the complete run timeseries and the ground truth/label information. * extractfeatures.m: Matlab script to extract feature information from all acoustic and seismic event timeseries for a given run and set of nodes. * sfm_mlpatterngen.m: Matlab script to extract feature information from esmic event timeseries. * ml_train1.m: Matlab script implementation of the Maximum Likelihood Training Module. ?ml_test1.m: Matlab script implementation of the Maximum Likelihood Testing Module. ?knn.m: Matlab script implementation of the K-Nearest Neighbor Classifier Module.
标签： acousticfeatures timeseries generate training
上传时间： 2013-12-26
上传用户：牛布牛
KNN. K- Neighbor Nearest Algorithm
标签： Algorithm Neighbor Nearest KNN
上传时间： 2017-07-18
上传用户：cx111111
How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.
标签： the decision clusters Cluster
上传时间： 2013-12-21
上传用户：gxmm
ClustanGraphics聚类分析工具。提供了11种聚类算法。 Single Linkage (or Minimum Method, Nearest Neighbor) Complete Linkage (or Maximum Method, Furthest Neighbor) Average Linkage (UPGMA) Weighted Average Linkage (WPGMA) Mean Proximity Centroid (UPGMC) Median (WPGMC) Increase in Sum of Squares (Ward s Method) Sum of Squares Flexible (ß space distortion parameter) Density (or k-linkage, density-seeking mode analysis)
标签： ClustanGraphics Complete Neighbor Linkage
上传时间： 2014-01-02
上传用户：003030
数字图像处理(K.R.Castkeman)
上传时间： 2013-06-18
上传用户：eeworm
