搜索资源列表
LOW-density-seperation
- 使用LDS(low density seperation)method 寻找最优分离面，然后对数据进行分类-Use LDS (low density seperation) method to find the optimal separation surface, and then classify the data
knn-classify
- 注释清楚，容易帮您理解k近邻分类的原理。采用c++语言编写-NOTE clear, easy to help you understand the principles of k nearest neighbors. Use c++ language
hua2
- 对150种鸢尾花数据进行分类的matlab代码-150 species of iris data to classify the matlab code
perception
- 用R语言进行机器学习中关于简单分类以及perception分类的代码。-some code about the easy classify and perception training in machine learning, using R.
question-one
- K-means进行文字图像分类，基础方法一-exploit K-means to classify the graphs
question-two
- K-means进行文字图像分类，中心谱聚类方法-spectral_clustering is used to classify the photos
kfold-knn.m.tar
- The program k folds the data set and applies K-Nearest Neighbors algorithm to classify the images.
Clustering
- 对数据进行归类，采用了k-means,NMF以及谱聚类三种方法。其中， 谱聚类的效率比较低下。-Classify the data, using the k-means, NMF and spectral clustering three methods. Among them, the relatively low efficiency of spectral clustering.
ypml110-dbscan-clustering
- 分类算法，实测可用，可选择分类算法类型进行分类。-Classification algorithm, found available, you can choose the type of classification algorithm to classify.
algorithm
- 多线性SVM分类器，在实现分类的同时，能很好的聚类-Multi-linear SVM classifier to classify the same time, can be a good clustering
bayes
- 首先对数据进行拆分，分为测试集与训练集，通过训练集进行贝叶斯网络的建模，最后利用建立的模型进行预测或分类任务的R语言代码-First, the data is split into a training set and test set, Bayesian network modeling through the training set, and finally the use of the model to predict or classify tasks R language code
literature
- 对采集的专家文献进行分类的一个自写的Java代码-The collection of the expert literature to classify a self written Java code
treePlotter
- 决策树根据信息增益对数据进行分类，并且构造树的结构，输出结果易于理解-Decision tree based on information gain to classify the data, and construct the structure of the tree, the output result is easy to understand
日常运动数据分析
- 用anaconda内部的科学库进行分析运动数据，并能对数据进行分类。(Anaconda's internal science library is used to analyze the motion data and to classify the data.)
文本深度挖掘
- 用于分析文档，分析情感指数，正负面情绪，及新闻分类(Used to analyze documents, analyze sentiment, positive and negative emotions, and classify news)
Boosting
- 分类，用于对数据进行归类。把数据按照不同的属性进行归类，并且使归类的精确度越高越好。(Classification; used to classify data. Classify data according to different attributes, and make the accuracy of classification better.)
EntropyBoost
- 用于对数据进行归类。把数据按照不同的属性进行归类，并且使归类的精确度越高越好。(used to classify data. Classify data according to different attributes, and make the accuracy of classification better.)
python-knn
- 主要利用Python软件，利用KNN算法对垃圾邮件进行分类(This paper mainly uses Python software to classify spam mail by using KNN algorithm)
Decision_tree-python
- 使用决策树（包括ID3,C45,CART）对数据做多分类预测。(Use Decision Tree to classify.)
PCA+mnist
- 基于python，利用主成分分析（PCA）和K近邻算法（KNN）在MNIST手写数据集上进行了分类。 经过PCA降维，最终的KNN在100维的特征空间实现了超过97%的分类精度。(Based on python, it uses principal component analysis (PCA) and K nearest neighbor algorithm (KNN) to classify on the MNIST handwritten data set. After PCA dime