3578577

收集的汉字编码相关的资料GBK2K-UNICODE，GBK 汉字内码扩展规范等(Collect the relevant information Chinese characters encoding GBK2K-UNICODE, GBK Chinese characters internal code specification etc.)

说明：  一般信道容量迭代算法 （1）初始化信源分布：P^((0))=(p_(1 ),p_(2 ),…,p_(i ),…,p_r)，置迭代计数器k=0，设信道容量相对误差门限为δ，δ>0，可设C(0)=-∞； （2）φ_ji^((k))=(p_ij p_i^((k)))/(∑p_ij p_i^((k)) ) i=1,…,r;j=1,…,s； （3）p_i^((k+1) )=exp?[∑_j?〖p_ij lnφ_ji^((k)) 〗]/(∑_i?〖{exp?[∑_j?〖p_ij lnφ_ji^((k)) 〗]}〗) i=1,…,r； （4）C^((k+1))=ln∑_i?〖{exp?[∑_j?〖p_ij lnφ_ji^((k)) 〗]}〗； （5）如果， (|C^((k+1) )-C^((k)) |)/C^((k+1)) ≤δ， 转向（7）； （6）否则，置迭代符合 k+1→k，转向（2）； （7）输出p_i^((k+1) )和C^((k+1))； （8）停止。(General channel capacity iterative algorithm)

一个系统自动升级程序，可远程自动更新系统。-Automatic escalation procedures, automatically update the system remotely.

振镜系统是一种由驱动板与高速摆动电机组成的一个高精度、高速度伺服控制系统，主要用于激光打标机控制。

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

乒乓操作FIFO，验证过的，用quartus II 写的！

【实例简介】非下采样剪切波变换（NSST）是对剪切波（shearlet）变换的改进，在继承剪切波（shearlet）变换优点的同时，避免了伪吉布斯现象的出现，相比于小波变换、曲边变换等操作效率高、复杂度低。该源代码中包含测试实例和代码注释。

该程序实现键盘对四相步进电机的控制，12864显示，利用ST188传感器测量步进电机转速。

本文件是供网络编程使用，里面还有许多实例。供初学者使用-This document is for the use of network programming, which there are many examples. For beginners to use

亚像素边缘提取、matlab代码。文档里包括插值法、拟合法、基于灰度矩法、基于zernike矩法