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流媒体播放器源码下载
该RTSP播放器实现了主流RTSP播放器的基本功能，并有所拓展： 1、RTSP 标准码流（包括音视频）的实时预览播放； 2、网络数据流的断线重连； 3、对存储文件的解码播放以及控制； 4、音视频码流的同步存储，文件存储时以当前系统的时间戳作为音视频 播放时的索引，方便在文件播放时以时间戳作为检索条件来点播文件； 5、视频播放格式上支持 h.264、mpeg4、mpeg2 等，音频播放格式上支 持 AAC、AMR、G711 等； 6、视频抓拍； 7、视频显示角度旋转； 8、画面填充控制显示比例。
- 2020-12-05下载
- 积分：1
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灰色预测模型代码
灰色预测模型GM（1，n）模型的matlab源代码，包括预测模型的建立，以及模型的精度检验指标c,p的计算。
- 2020-11-27下载
- 积分：1
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实时风云二号卫星云图.rar
【实例简介】实时风云二号卫星云图，带动画自动播放，显示当前时间前12小时和24小时的卫星云图！免费资源，方便大家使用！
- 2021-11-21 00:47:35下载
- 积分：1
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软件模拟IIC主从机
使用单片的普通IO口模拟IIC的主机和从机，自己由此开始，感觉归纳总结的很好，代码也很具有参考性
- 2020-12-06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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2套经典毕业论文答辩ppt模板
2套经典毕业论文答辩ppt模板
- 2021-05-07下载
- 积分：1
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STM32 人脸识别系统
单片机STM32利用OV2640实现人脸识别实验，keil5工程文件。
- 2021-05-06下载
- 积分：1
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小波阈值去噪的程序，希望对小波初学者较大帮助。
小波阈值去噪的程序，包括默认阈值去噪、全局阈值去噪、自适应阈值去噪。
- 2021-05-06下载
- 积分：1
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数据挖掘18大算法实现以及其他相关经典DM算法
数据挖掘算法算法目录18大DM算法包名 目录名 算法名AssociationAnalysis DataMining_Apriori Apriori-关联规则挖掘算法AssociationAnalysis DataMining_FPTree FPTree-频繁模式树算法BaggingAndBoosting DataMining_AdaBoost AdaBoost-装袋提升算法Classification DataMining_CART CART-分类回归树算法Classification DataMining_ID3 ID3-决策树分类算法Classification Da
- 2020-12-12下载
- 积分：1
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Strapdown analytics _Paul G Savage
关于捷联惯导的一本很经典的英文著作，适合相关方向的研究生和科研人员使用。此处将本书分为两个部分，均放入压缩包中。
- 2021-05-07下载
- 积分：1
