DTMF

说明：  matlab源代码 C4.5算法 经典算法 这里有具体的操作 是为.M文件 (C4.5 algorithm matlab source code there is the classical algorithm for the specific operation. M files)

Bagged Tree. Machine Learning. Algorithm. Code. Matlab

特徵粹取（feature extraction）是特徵選取（feature selection）的延伸，簡單地說，我們希望將資料群由高維度的空間中投影到低維度的空間，因此，我們必須找出一組基底向量（base）來進行線性座標轉換，使得轉換後的座標，能夠符合某一些特性。 我們可以將特徵粹取分成「包含類別資訊」和「不包含類別資訊」兩大類。包含類別資訊指的是我們已經知道哪些資料分別歸屬於哪一類；而不包含類別資訊的特徵粹取則適用於我們不知道手上的資料點分別該歸屬於哪一類，甚至連該劃分成幾類都不知道的情況。對於這兩大類資料，可以分述如下： 對於「不包含類別資訊」的資料，我們通常使用「主要分量分析」（principal component analysis），簡稱 PCA。 (Cuiqu features (feature extraction) is feature selection (feature selection) extension, simply put, we want to base the projection data the high-dimensional space to low-dimensional space, so we have to find a set of basis vectors ( base) to the linear coordinate conversion, so that after the coordinate conversion, can meet a number of features. We can feature Cuiqu into " category contains information" and " does not contain a category of information," two categories. Contains categories of information refers to what we already know what kind of information are attributed to Cuiqu features include categories of information are not applicable to information we do not know the points are in the hands of the home in which category, and even that is divided into several class do not know the situation. For these two categories of information, can be divided as follows: For information " does not include the categories of information" , we usually use the " )

主成分分析法，主要用在光谱处理上的，包括可见和近红外光谱，对于其他的一些统计学数据也比较适用，包括原数据和处理后的数据(Principal component analysis (pca), mainly used on spectrum processing, including visible and near infrared spectrum, and is also applicable for other statistical data)

quiver plot in matlab

说明：  针对四维目标跟踪模型的主程序，可以用来学习仿真模型参数(2017-ITAC-A Novel Adaptive Kalman Filter With Inaccurate Process and Measurement Noise Covariance Matrices)

说明：  基于MATLAB的平面六连杆机构运动分析(MATLAB-based planar six-Linkage Kinematic Analysis)

For 3d Photonic crystals with diamond structure. Calculation of photonic band gaps etc. using matlab

panel plot toolbox for subplots

Physical Modeling in MATLAB ebook