data mining k nearest neighbour
標簽： neighbour nearest mining data
上傳時間： 2017-09-15
上傳用戶：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.
標簽： classification different document performs
上傳時間： 2015-03-03
上傳用戶：希醬大魔王
在visual basic環境下，實現k-nearest neighbor算法。
上傳時間： 2013-12-08
上傳用戶：ma1301115706
KNN算法的實現，k-nearest neighbors聚類算法的matlab 實現
上傳時間： 2013-12-19
上傳用戶：AbuGe
樸素貝葉斯(Naive Bayes, NB)算法是機器學習領域中常用的一種基于概率的分類算法，非常簡單有效。k近鄰法(k-nearest Neighbor, kNN)[30,31]又稱為基于實例(Example-based, Instance-bases)的算法，其基本思想相當直觀：Rocchio法來源于信息檢索系統，后來最早由Hull在1994年應用于分類[74]，從那以后，Rocchio方法就在文本分類中廣泛應用起來。
上傳時間： 2014-01-03
上傳用戶：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.
標簽： acousticfeatures timeseries generate training
上傳時間： 2013-12-26
上傳用戶：牛布牛
KNN. K- Neighbor Nearest Algorithm
標簽： Algorithm Neighbor Nearest KNN
上傳時間： 2017-07-18
上傳用戶：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.
標簽： the decision clusters Cluster
上傳時間： 2013-12-21
上傳用戶：gxmm
ClustanGraphics聚類分析工具。提供了11種聚類算法。 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)
標簽： ClustanGraphics Complete Neighbor Linkage
上傳時間： 2014-01-02
上傳用戶：003030
數字圖像處理(K.R.Castkeman)
上傳時間： 2013-06-18
上傳用戶：eeworm
