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released
- 本文件是我自己写的决策树的一个例子，很适合初学者学习，用决策树分类，实现很简单-This document is an example of the decision tree of my own writing, it is suitable for beginners to learn, decision tree classification, to achieve a very simple
pca_exercise
- 这是一份介绍PCA在图像处理里面的例子，里面代码都有详细介绍，很有价值-This is a descr iption of the image processing inside PCA example, which codes are detailed, great value
KNN
- K-最邻近分类器的一个实例，实现了对数据的分类，内含测试数据-an example of K-nearest algorithm,containing a set of test data
k-means
- k-means算法的一个小实例，很好的展示了，算法的过程，测试聚类文件在txt中-A small example k-means algorithm, a good showing, the algorithm process, the test cluster file txt
kmeans
- k-means算法的一个小实例，很好的展示了，算法的过程-k-means algorithm for a small example, a good showing, the algorithm process
BasicELM
- 用一个简短的例子说明了随机权神经网络算法，算是对深度学习的入门的一个小的巩固-With a brief example illustrates the random weights neural network algorithm, considered for entry-depth study of a small consolidation
DecisionTree
- 本程序是利用python写的一个决策树算法，通过该例子可以实现简单的决策树处理，也可以学习决策树算法的基本思想。-This procedure is to use python to write a decision tree algorithm, this example can be achieved by a simple decision tree processing, you can also learn the basic idea of the decision tree alg
KNN-implement-by-python
- 该程序是用python编写一个K近邻算法，通过该例子可以掌握K近邻算法，是学习数据挖掘的一个高效的算法。-The program is written in python a K-nearest neighbor algorithm, this example can grasp the K-nearest neighbor algorithm, a learning data mining and efficient algorithms.
LogisticRegression
- 本例是用Python写的简单的逻辑回归的例子，可以下载试试。-This case is an example of a simple logistic regression written in Python, you can download a try.
svmMLiA
- 支持向量机是最常用的一种分类器，它通过求解一个二次优化问题来最大化分类间隔，本例采用的SMO算法，可以大大优化运行-Support vector machine is the most commonly used classifier, it can be used to solve a two optimization problem to maximize the classification interval, this example uses the SMO algorithm, ca
MLRforSSVEPDemo
- 对于CK信号进行降维，同时也有线性回归方法，里面有一个例子供大家学习-CK signal for dimensionality reduction, but also a linear regression method, which has an example for everyone to learn
Naive-bayes
- 本文以拼写检查作为例子，讲解Naive Bayes分类器是如何实现的。对于用户输入的一个单词（words），拼写检查试图推断出最有可能的那个正确单词（correct）。当然，输入的单词有可能本身就是正确的。比如，输入的单词thew，用户有可能是想输入the，也有可能是想输入thaw。为了解决这个问题，Naive Bayes分类器采用了后验概率P(c|w)来解决这个问题。P(c|w)表示在发生了w的情况下推断出c的概率。为了找出最有可能c，应找出有最大值的P(c|w)，即求解问题-In this
C4.5
- C4.5算法的matlab实现，里面有标准数据集作为实例进行演示-C4.5 algorithm matlab implementation, which has a standard data set as an example to demonstrate
svm-classification
- 此文件是利用支持向量机解决分类预测问题的一个简单的例子-This document is a simple example of using support vector machines to solve classification prediction problems