LDA与PCA的讲解与matlab演示

很多朋友在用keil仿真时，有时编译不成功，出现错误提示:*** FATAL ERROR L210: I/O ERROR ON INPUT FILE: EXCEPTION 0021H: PATH OR FILE NOT FOUND FILE: E:KEILC51LIBC51FPS.LIB ,这是由于虽然math.h这个头文件添加进去了，但是很多朋友装的keil c不是完整版本，缺少c51fps.lib这个进行浮点运算的库文件，下载这个库文件添加keil c的库中就可以编译成功了。

液位测量广泛应用于工业、经济、生活等领域。本设计以水箱供水为模型，用于对水箱液位信号进行测量监控记录。基于单片机的液位测量装置具有测量准确、重复性好、功耗低、使用寿命长的特点，是广泛采用的技术。在深入学习科学发展观的同时，电子设备的设计也需融入可持续发展的设计理念。故此，在基于单片机的液位测量装置基础上，扩展实时监控、数据采集、计算机串行通信等功能，从而能够通过科学的方法将液位测量与统计科学结合，合理调度水资源，降低能源消耗。本文从系统方案选择与论证，硬件电路设计，系统软件与上位机软件设计等几个方面介绍了基于单片机的液位测量监控系统的设计过程，最终实现了液位的实时测量与监控。最后，本文总结

一个关于图像去雾的基本算法代码的超强整合，每一行（划重点）代码都有详细而好懂的注释，全部都是m文件，另外还有许多测试图例，从浓雾到薄雾都有。暗通道方法采用改进算法，增加了图像采样，提升了计算速度，改进了导向滤波最小二乘的算法使其精细透视率图的效果得到了显著提升。另外附带相应根据上述代码编写的GUI设计的m文件

提取了将近10种时域特征值，包括平均值，方差，均方根，四分位间距等

计算机系毕业论文智能化小区网络设计规.doc计算机系毕业论文智能化小区网络设计规.doc

matlab环境下光伏电池典型模型光照变化能影响光伏电池的输出特性曲线

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

数据融合的资料不多，本文提供了一些数据融合、信息融合的PPT资料，希望对有志于信号处理、信息融合的同仁有所帮助！多传感器问题的引入数据融合定义数据融合的应用数据融合技术发展

用C++实现的压缩、解压缩算法，只有几个cpp、h文件，非常小巧，方便学习算法用。

主要用MATLAB编写的核密度估计方法，采用的核是高斯核！