随机森林工具包randomforest-matlab(基于RF_MexStandalone-v0.02修改,保证可用)

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The Verilog® Hardware Description Language (Verilog HDL) became an IEEE standard in 1995 as IEEEStd 1364-1995. It was designed to be simple, intuitive, and effective at multiple levels of abstraction in astandard textual format for a variety of design tools, including verification simulation, timiThe clear directive from the users for these three task forces was to start by solving some of the followingproblemsConsolidate existing IeeE Std 1364-1995Verilog generate statementMulti-dimensional arraysEnhanced Verilog file i/oRe-entrant tasksStandardize Verilog configurationsEnhance timing representationEnhance the vpi routinesAchievementsOver a period of four years the 1364 Verilog Standards Group(vsg) has produced five drafts of the lrmThe three task forces went through the EEe Std 1364-1995 lRM very thoroughly and in the process of consolidating the existing Lrm have been able to provide nearly three hundred clarifications and errata for theBehavioral, ASIC, and PLI sections. In addition, the vsg has also been able to agree on all the enhance-ments that were requested (including the ones stated above)Three new sections have been added. Clause 13, "Configuring the contents of a design, deals with configuration management and has been added to facilitate both the sharing of verilog designs between designersand/or design groups and the repeatability of the exact contents of a given simulation session Clause 15Timing checks, "has been broken out of Clause 17, "System tasks and functions, "and details more fullhow timing checks are used in specify blocks. Clause 16, "Backannotation using the Standard Delay Format(SDF), addresses using back annotation(IEEE Std 1497-1999)within IEEE Std 1364-2001Extreme care has been taken to enhance the vpi routines to handle all the enhancements in the behavioraland other areas of the lrm. minimum work has been done on the pli routines and most of the work hasbeen concentrated on the vpi routines. Some of the enhancements in the vpi are the save and restart simu-lation control, work area access, error handling, assign/deassign and support for array of instances, generateand file 1/0Work on this standard would not have been possible without funding from the cas society of the ieee andOpen verilog InternationalThe IEEE Std 1364-2001 Verilog standards Group organizationMany individuals from many different organizations participated directly or indirectly in the standardizationprocess. The main body of the Ieee Std 1364-2001 working group is located in the United States, with asubgroup in Japan (EIAJ/1364HDL)The members of the IEEE Std 1364-2001 working group had voting privileges and all motions had to beapproved by this group to be implemented the three task forces focused on their specific areas and theirrecommendations were eventually voted on by the Ieee Std 1364-2001 working group

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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从开题报告，到毕业论文，最后的论文答辩的ppt，全套资料，也是本人的倾心之做，毕业论文模式，和毕业设计主题 欢迎大家借鉴