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matlab的图像缩放和旋转代码
本代码是matlab的图像缩放和旋转代码，里面使用了最近领域实现和线性插值法实现算法实现图像缩放和旋转
- 2020-12-11下载
- 积分：1
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C语言-遗传算法的排课源码
C语言-遗传算法的排课源码，遗传算法、使用c语言开发，源码
- 2020-12-05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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经典开环子空间辨识
子空间辨识，来自线性系统子空间辨识这本书，matlab程序，简单好用。
- 2020-12-03下载
- 积分：1
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Fisherface的原始论文和完整的源代码（matlab）（包含数据库）
该内容为fisherface的matlab代码，绝对可以运行，实验的数据库为ORL人脸数据库，并且在代码中很多中文注释，便于理解，为了大家方便，在压缩包中还放了fisherface的原始原始论文和PCA算法的原始论文。希望这些资料对大家有所帮助。
- 2020-12-10下载
- 积分：1
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盲信源分离可变步长自然梯度算法
用可变步长的自然梯度算法解决盲信源分离问题。
- 2020-12-05下载
- 积分：1
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捷联惯导算法仿真
【实例简介】捷联惯导算法，仿真程序，，c语言源代码，对学习导航算法的同学很有帮助
- 2021-11-03 00:34:17下载
- 积分：1
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车载网络共享资源分配matlab程序
随着5g的发展，越来越多的资源共享问题也成为了研究热点，描述了5g中车载网络的资源分配问题，
- 2021-05-06下载
- 积分：1
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莫防破解模块2.0，支持全系统隐藏进程，保护进程，隐藏模块，支持win7 64位隐藏进程，，支持win8 64位隐藏进程，支持win10 64位隐藏进程，
莫防破解模块2.0，支持全系统隐藏进程，保护进程，隐藏模块，支持win7 64位隐藏进程，隐藏模块，支持win8 64位隐藏进程，隐藏模块支持win10 64位隐藏进程，隐藏模块如发现个别机子隐藏时 蓝屏，请记住 开始蓝屏的时长，可写个循环，例如20分钟蓝屏，便20分钟取消隐藏进程，然后重新加载隐藏进程
- 2020-12-04下载
- 积分：1
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模型预测仿真
这是逆变器模型预测基本仿真，可以学习一下。
- 2020-12-09下载
- 积分：1
