船舶海上运动matlab仿真

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NPOI操作word、excel、ppt等文件的利器。

火力发电厂热平衡程序计算—matlab（64位），用于计算火电厂全厂的各种指标比如发电煤耗、热耗等指标，方便火电厂前期设计计算和后期校核

1-15节全部完整版讲义！超清分享~~~（附赠目录索引和NG原版讲义）含金量高，独家整理~~目录如下：公开课笔记1-2——线性规划、梯度下降、正规方程组公开课笔记3——局部加权回归、逻辑斯蒂回归、感知器算法公开课笔记4——牛顿方法、指数分布族、广义线性模型公开课笔记5——生成学习、高斯判别、朴素贝叶斯公开课笔记6——NB多项式模型、神经网络、SVM初步公开课笔记7——最优间隔分类、原始/对偶问题、SVM对偶公开课笔记8———核技法、软间隔分类器、SMO算法公开课笔记9—偏差/方差、经验风险最小化、联合界、一致收敛公开课笔记10——VC维、模型选择、特征选择公开课笔记11

聚类分析Matlab工具箱 Version1.0Clustering Analysis Matlab Toolbox - Trial Version 1.0%------------------------------------------------------------------------------1、该工具箱包括了常用的三种K-Means聚类分析和ISODATA聚类分析:...KMeansMain_KMeans.m % 标准 K-Means 聚类 - 主函数...KMeansMain_FuzzyKMeans.m

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基于文本与图像(BMP和JPG格式)的信息隐藏算法(LSB,DCT)的实现,调试无误

本书首先介绍TMATLAB语言程序设计的基本内容，在此基础上系统地介绍了系统仿真所必要的数值计算方法及MATLAB实现，并以Simulink为主要工具介绍了系统仿真方法与技巧，包括连续系统、离散系统、随机输入系统和复数系统的仿真，由浅入深地介绍了模块封装技术、复杂模型的线性化、多领域物理建模思想及工程系统仿真、非工程系统建模与仿真、Stateflow有限状态机及离散事件系统建模与仿真等中高级使用方法。最后还介绍了半实物仿真技术与实时控制技术。　　本书可作为一般读者学习和掌握MATLAB/Simulink语言的教科书。也可作为高校理工科各类专业的本科生和研究生系统仿真类课程的教材和参考书，还可

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

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