SourceInsight 完美的配色方案 theme_Monokai 主题

这是用马尔克夫模型进行数据预测预测的数据是数学建模中 流感疫苗的爆发情况

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

用人工智能领域中经典的启发式算法实现了人工智能中的十五数码问题。包括详细的实验报告和源代码，源码由C#可视化编写，debug中有编译好的程序，界面友好。另注：船院6系学生不要下载，避免雷同。

【实例简介】任何事务都是一分为二的有利就有弊我们发现现在越来越多的工程师不关心自己的电路实现形式以为我只要将功能描述正确其它事情交给工具就行了在这种思想影响下工程师在用HDL语言描述电路时脑袋里没有任何电路概念或者非常模糊也不清楚自己写的代码综合出来之后是什么样子映射到芯片中又会是什么样子有没有充分利用到FPGA的一些特殊资源遇到问题立刻想到的是换速度更快容量更大的FPGA器件导致物料成本上升更为要命的是由于不了解器件结构更不了解与器件结构紧密相关的设计技巧过分依赖综合等工具工具不行自己也就束手无策导致问题迟迟不能解决从而严重影响开发周期导致开发成本急剧上升。 目前我们的设计规模越来越庞大动辄上百万门几百万门的电路屡见不鲜同时我们所采用的器件工艺越来越先进已经步入深亚微米时代而在对待深亚微米的器件上我们的设计方法将不可避免地发生变化要更多地关注以前很少关注的线延时我相信ASIC设计以后也会如此此时如果我们不在设计方法设计技巧上有所提高是无法面对这些庞大的基于深亚微米技术的电路设计而且现在的竞争越来越激励从节约公司成本角度出发也要求我们尽可能在比较小的器件里完成比较多的功能。 本文对读者的技能基本要求是熟悉数字电路基本知识如加法器计数器RAM等熟悉基本的同步电路设计方法熟悉HDL语言对FPGA的结构有所了解对FPGA设计流程比较了解。

这是压缩感知技术理论的代码程序，包含了MP，OMP，OMPCHOL，OMPHI多种压缩重构算法，非常不错。

本程序是自己针对一篇IEEE TCST文章，用matlab编程实现，已验证可以运行。附件有详细的程序使用说明，和对应的文章。适合多智能体的编队或一致性研究的初学者学习。(这个程序上传的时候少了一个m文件，请搜索本人上传的所有资源找到补充文件) 程序使用说明：1、首先运行Dong2015IEEECST.m2、再运行Dong2015IEEECST1.slx3、最后运行PLOT_Dong.m

本科毕业设计，基于springboot框架的宿舍管理系统设计及实现。项目功能完备，文档资料等齐全。

自己的毕业设计，当时可是获得优秀毕业论文的.本课题预期实现一个基于VC++/Access的宿舍管理系统。用户交互界面友好，操作方便、快捷，保证各类数据信息能实现正确、及时的存储以及快速查询，实现对宿舍的高效管理。系统预期实现以下功能： 1、用户登录2、学生信息管理（1）添加入住学生（2）学生信息查询（3）学生信息修改（4）学生离校登记等3、来访人管理4、宿舍管理（1）查看空宿舍、空铺 （2）学生调换宿舍 （3）卫生评分 （4）维修登记 等5、后勤管理 （1）添加管理员 （2）管理员考勤等

站里这方面资源不多，特别是种子区域生长方面的很少，上传一个算一个吧rightnumleftnuIm percent2()Ivaluerightnumipercent2(rvalue)pm= lvalue/‖P‖,pma= rvalue/‖P‖‖Pwindows xpMATLAB (R2008aOtsa.RlenI=ll, Rlen2=60, percent1=55%c, percent270%b. Rlenl=ll, Rlen2=80, percent1=50%, percent2=94%c.Rlen1-1l, Rlen2-100 percent1-65%. percent2-80%d. Rlen1-1l, Rlen2-=80, percent1=60%. percent2-83%2cent 1percent2Percentppercent 13C1994-2012ChinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhtlp://www.cnki.net[J]2007,29(6):854-857[J]26(2):93-962010,45(7):76-80[JI.1963:1l5-12005,26(11):[J],2005,17(1):1-3Research on the improved algorithm and application ofseed region growth method and its sail image extrationYAN Shen-hai, HUANG Xian-tong, LIU Yang(School of Mathematics Computer Science, Gannan Normal University, Ganzhou 341000, Jiangxi, ChinaAbstract: Seeded region growing is a common method of image segmentation. Its performance depends largelyon the seleclion of seed points and growth rules. In order Lo extract snail images with such methods more effectively.The article analyzes the basic idea of the seeded region growing method(SRG) and presents an improved seededregion growing method (ISRG) which is used to extract the snail images. In ISRG, novel similarities rules and anew dynamic threshold method are adopted. For the complex and various habitat of snail, ISRG selects the region growing seed points manually. It is demonstrated by experimental results that ISRG can achieve better snailimage extraction results under the complicated real-world sceneKey words: seeded region growing (SRG; image extraction; dynamic threshold; snail(E D:X, JC1994-2012ChinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhtlp://www.cnki.net

基于蒙特卡罗法的二维随机裂隙Matlab程序，需要输入参数。