ACTRAN声学教程

数据挖掘在淘宝卖家评论中的应用，数据挖掘和文本挖掘实例

matlab BPSK 2PSK QPSK 4PSK，误码率，星座图

文字版PDF+随书源代码以及工具每一种处理器都有它自己的机器指令集，而汇编语言的发明则是为了方便这些机器指令的记忆和书写。尽管汇编语言已经较少用于大型软件程序的开发，但从学习者的角度来看，要想真正理解计算机的工作原理，掌握它内部的运行机制，学习汇编语言是必不可少的。本书采用开源的NASM汇编语言编译器和VirtualBox虚拟机软件，以个人计算机广泛采用的Intel处理器为基础，详细讲解了Intel处理器的指令系统和工作模式，以大量的代码演示了16/32/64位软件的开发方法，介绍了处理器的16位实模式和32位保护模式，以及基本的指令系统。这是一本有趣的书，它没有把篇幅花在计算一些枯燥的数学

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

Mann-kendall趋势分析的Matlab程序

【实例简介】Ksend是一个自动化工具，可以应用于自动化测试、设备维护等领域，它可以模拟后台文本消息的发送、模拟键盘和鼠标动作、并能进行简单的windows界面、IE页面的分析和控制等。 Ksend的主要功能： ² 文本发送功能：将文本分行发送至指定的一个或多个文本窗口，可以控制发送的延时； ² 支持键盘、鼠标的录制和重放； ² 支持TCL语言控制，可以利用TCL语言控制键盘、鼠标、界面及文本的发送； ² 可以利用TCL语言分析、捕获和控制窗口； ² 支持IE页面的分析和控制； 应用场景： 命令行维护、测试：可以通过TCL语言或批语句将维护或测试命令发送到telnet终端、DOS窗口、串口终端等窗口，实现自动化的配置和维护。比如添加10000条路由、10000条ACL等参数遍历的维护和测试；可以进行局域网地址的连通性遍历测试；可以用于设备的批量升级维护等等。 界面维护、测试：可以利用程序中扩展的TCL语句对界面进行控制，比如在WEB界面添加1000个用户、自动发送Notes邮件等等。

《零死角玩转 STM32》系列教程由初级篇、中级篇、高级篇、系统篇、四个部分组成，根据野火 STM32 开发板旧版教程升级而来，且经过重新深入编写，重新排版，更适合初学者，步步为营，从入门到精通，从裸奔到系统，让您零死角玩转 STM32。

用matlab打开该项目并导入路径后 运行gui.m首先点击读入照片按钮并导入照片 然后点击人脸识别按钮

本资源包括，基于SEIR模型的新冠肺炎疫情分析matlab代码和最新的国内疫情数据集。代码已详细备注

自己编写的matlab自适应干扰对消程序，通过互相关找匹配相位，再找对消系数