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中央大学 EMD HHT Matlab 源代码
此资源为黄老师推荐的，中央大学数据研究中心提供EMD 代码，其中有EMD和EEMD的算法，在本人的资源里也有提供G-Rilling的开源代码，大家可自行选择，在我的博客中，我有详细介绍如何安装。www.cnblogs.com/BrowserSnake ， 祝好，Allen.
- 2020-12-06下载
- 积分：1
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8邻域算法边缘跟踪
图像边缘获取，自己实现的基于matlab的8邻域算法，获取图像的边缘。需要手动设置阈值对图像进行二值化。包涵实现程序和测试程序
- 2020-11-30下载
- 积分：1
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SSH真实项目源码（java）+所有开发文档（全）.rar
【实例简介】由于项目很大，所以上传的不包含JAR包，请自己添加进来。里面包括了所有的开发文档
- 2021-11-24 00:50:01下载
- 积分：1
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matlab虹膜识别
虹膜识别中的特征提取算法：2D-Gabor滤波器算法，将归一化的虹膜图像由此滤波器滤波，可以得到二进制的特征编码。效果很好
- 2020-12-11下载
- 积分：1
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高铁梅《计量经济分析方法与建模Eviews应用与实例》（第二版）的数据和课件
高铁梅《计量经济分析方法与建模Eviews应用与实例》（第二版）的数据和课件 PPT好东西。
- 2021-05-06下载
- 积分：1
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基于颜色特征形状特征和纹理特征的数字图像的检索（Digital Image Retrieval）MATLAB GUI实现
该程序实现的功能为：检索出指定的图像文件，并从检索出的图像中检索出指定的物体 。1,主程序为Run.m和Run.fig。2、颜色特征，形状特征，纹理特征为对应的各子程序。3、图像库为Corel1。如有不明之处，可联系Email至hqucuihao@163.com交流。
- 2020-12-10下载
- 积分：1
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PWM电机调速原理及51单片机PWM程序经典
大家一起看看，希望对大家有用！PWM电机调速原理及51单片机PWM程序经典
- 2020-12-10下载
- 积分：1
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黑金spartan6教程
【实例简介】黑金spartan6学习教程，写的很好，很详细，很适合初学者的学习
- 2021-11-18 00:49:59下载
- 积分：1
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【PDF】《Machine learning A Probabilistic Perspective》 MLAPP；by Kevin Murphy
完整版，带目录，机器学习必备经典；大部头要用力啃。Machine learning A Probabilistic PerspectiveMachine LearningA Probabilistic PerspectiveKevin P. MurphyThe mit PressCambridge, MassachusettsLondon, Englando 2012 Massachusetts Institute of TechnologyAll rights reserved. No part of this book may be reproduced in any form by any electronic or mechanicalmeans(including photocopying, recording, or information storage and retrieval)without permission inwriting from the publisherFor information about special quantity discounts, please email special_sales@mitpress. mit. eduThis book was set in the HEx programming language by the author. Printed and bound in the UnitedStates of AmLibrary of Congress Cataloging-in-Publication InformationMurphy, Kevin Png:a piobabilistctive/Kevin P. Murphyp. cm. -(Adaptive computation and machine learning series)Includes bibliographical references and indexisBn 978-0-262-01802-9 (hardcover: alk. paper1. Machine learning. 2. Probabilities. I. TitleQ325.5M872012006.31-dc232012004558109876This book is dedicated to alessandro, Michael and stefanoand to the memory of gerard Joseph murphyContentsPreactXXVII1 IntroductionMachine learning: what and why?1..1Types of machine learning1.2 Supervised learning1.2.1Classification 31.2.2 Regression 83 Unsupervised learning 91.3.11.3.2Discovering latent factors 111.3.3 Discovering graph structure 131.3.4 Matrix completion 141.4 Some basic concepts in machine learning 161.4.1Parametric vs non-parametric models 161.4.2 A simple non-parametric classifier: K-nearest neighbors 161.4.3 The curse of dimensionality 181.4.4 Parametric models for classification and regression 191.4.5Linear regression 191.4.6Logistic regression1.4.7 Overfitting 221.4.8Model selection1.4.9No free lunch theorem242 Probability2.1 Introduction 272.2 A brief review of probability theory 282. 2. 1 Discrete random variables 282. 2.2 Fundamental rules 282.2.3B292. 2. 4 Independence and conditional independence 302. 2. 5 Continuous random variable32CONTENTS2.2.6 Quantiles 332.2.7 Mean and variance 332.3 Some common discrete distributions 342.3.1The binomial and bernoulli distributions 342.3.2 The multinomial and multinoulli distributions 352. 3.3 The Poisson distribution 372.3.4 The empirical distribution 372.4 Some common continuous distributions 382.4.1 Gaussian (normal) distribution 382.4.2Dte pdf 392.4.3 The Laplace distribution 412.4.4 The gamma distribution 412.4.5 The beta distribution 422.4.6 Pareto distribution2.5 Joint probability distributions 442.5.1Covariance and correlation442.5.2 The multivariate gaussian2.5.3 Multivariate Student t distribution 462.5.4 Dirichlet distribution 472.6 Transformations of random variables 492. 6. 1 Linear transformations 492.6.2 General transformations 502.6.3 Central limit theorem 512.7 Monte Carlo approximation 522.7.1 Example: change of variables, the MC way 532.7.2 Example: estimating T by Monte Carlo integration2.7.3 Accuracy of Monte Carlo approximation 542.8 Information theory562.8.1Entropy2.8.2 KL dive572.8.3 Mutual information 593 Generative models for discrete data 653.1 Introducti653.2 Bayesian concept learning 653.2.1Likelihood673.2.2 Prior 673.2.3P683.2.4Postedictive distribution3.2.5 A more complex prior 723.3 The beta-binomial model 723.3.1 Likelihood 733.3.2Prior743.3.3 Poster3.3.4Posterior predictive distributionCONTENTS3.4 The Dirichlet-multinomial model 783. 4. 1 Likelihood 793.4.2 Prior 793.4.3 Posterior 793.4.4Posterior predictive813.5 Naive Bayes classifiers 823.5.1 Model fitting 833.5.2 Using the model for prediction 853.5.3 The log-sum-exp trick 803.5.4 Feature selection using mutual information 863.5.5 Classifying documents using bag of words 84 Gaussian models4.1 Introduction974.1.1Notation974. 1.2 Basics 974. 1.3 MlE for an mvn 994.1.4 Maximum entropy derivation of the gaussian 1014.2 Gaussian discriminant analysis 1014.2.1 Quadratic discriminant analysis(QDA) 1024.2.2 Linear discriminant analysis (LDA) 1034.2.3 Two-claSs LDA 1044.2.4 MLE for discriminant analysis 1064.2.5 Strategies for preventing overfitting 1064.2.6 Regularized LDA* 104.2.7 Diagonal LDA4.2.8 Nearest shrunken centroids classifier1094.3 Inference in jointly Gaussian distributions 1104.3.1Statement of the result 1114.3.2 Examples4.3.3 Information form 1154.3.4 Proof of the result 1164.4 Linear Gaussian systems 1194.4.1Statement of the result 1194.4.2 Examples 1204.4.3 Proof of the result1244.5 Digression: The Wishart distribution4.5. 1 Inverse Wishart distribution 1264.5.2 Visualizing the wishart distribution* 1274.6 Inferring the parameters of an MVn 1274.6.1 Posterior distribution of u 1284.6.2 Posterior distribution of e1284.6.3 Posterior distribution of u and 2* 1324.6.4 Sensor fusion with unknown precisions 138
- 2020-12-10下载
- 积分：1
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三维重建代码
傅里叶变换积分法-frankotchellapa.m。matlab代码和文献
- 2020-12-01下载
- 积分：1
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