使用Opencv库实现多种类型图片批量互相转换，包括 tif bmp jpg pgm png五种类型的图片。
2021-03-06 14:47:25
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达芙妮 Koller 2009年大作，不用多说，概率图模型经典，因为是从国外网站购买的高清版pdf，所以有偿分享一下。 （绝对不是扫描板！请放心下载！1270页）
2020-01-25 03:13:32
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Daphne Koller 关于Probabilistic Graphical Models 的最权威大作，内容详实深入，是各大名校机器学习和人工智能专业相应课程的指定教材
Adaptive Computation and Machine LearningThomas dietterich, EditorChristopher Bishop, David Heckerman, Michael Jordan, and Michael Kearns, Associate EditorsBioinformatics: The Machine learning Approach, Pierre Baldi and Soren BrunakReinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. BartoGraphical models for Machine Learning and Digital Communication, Brendan J. FreyLearning in graphical Models, Michael I. JordanCausation, Prediction, and Search, 2nd ed, Peter Spirtes, Clark Glymour, and Richard ScheinesPrinciples of Data Mining, David Hand, Heikki Mannila, and Padhraic SmythBioinformatics: The Machine Learning Approach, 2nd ed, Pierre Baldi and Soren BrunakLearning Kernel classifiers: Theory and Algorithms, Ralf HerbrichLearning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, Bernhard Scholkopf and Alexander J smolaIntroduction to Machine Learning, Ethem AlpaydinGaussian Processes for Machine Learning, Carl Edward Rasmussen and Christopher K.I. WilliamsSemi-Supervised Learning, Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien, edsThe Minimum description Length Principle, Peter D GrunwaldIntroduction to Statistical Relational Learning, lise Getoor and Ben Taskar, edsProbabilistic Graphical Models: Principles and Techniques, Daphne Koller and Nir FriedmanProbabilistic Graphical ModelsPrinciples and TechniquesDaphne kollerNir friedmanThe mit pressCambridge, MassachusettsLondon, England@2009 Massachusetts Institute of TechnologyAll rights reserved. No part of this book may be reproduced in any form by any electronicor mechanical means (including photocopying, recording, or information storage and retrieval)without permission in writing from the publisherFor information about special quantity discounts, please email special_sales@mitpress. mit. eduThis book was set by the authors in BlFX2EPrinted and bound in the united states of americaLibrary of Congress Cataloging-in-Publication DataKoller, DaphneProbabilistic Graphical Models: Principles and Techniques Daphne Koller and Nir Friedmanp cm. -(Adaptive computation and machine learning)Includes bibliographical references and indexisBn 978-0-262-01319-2(hardcover: alk. paper1. Graphical modeling(Statistics) 2. Bayesian statistical decision theory--Graphic methods. IKoller, Daphne. II. Friedman, NirQA279.5.K652010519.5’420285-dc222009008615109876543To our familiesmy parents Dov and ditzamy husband danmy daughters natalie and mayaDKmy parents Noga and Gadmy wifemy children roy and liorMEAs far as the laws of mathematics refer to reality, they are not certain, as far as they arecertain, they do not refer to realityAlbert einstein 1956When we try to pick out anything by itself, we find that it is bound fast by a thousandinvisible cords that cannot be broken, to everything in the universeJohn Muir, 1869The actual science of logic is conversant at present only with things either certain, impossible,or entirely doubtful . Therefore the true logic for this world is the calculus of probabilities,which takes account of the magnitude of the probability which is, or ought to be, in areasonable man's mindJames Clerk Maxwell, 1850The theory of probabilities is at bottom nothing but common sense reduced to calculus;itenables us to appreciate with exactness that which accurate minds feel with a sort of instinctfor which ofttimes they are unable to account.Pierre Simon Laplace, 1819Misunderstanding of probability may be the greatest of all impediments to scientific literacyStephen Jay GouldContentsAcknowledgmentsList of figuresList of algorithmsList of boxesXXX1 IntroductionL1 Motivation 11.2 Structured Probabilistic Models 21.2.1 Probabilistic Graphical Models 31.2.2 Representation, Inference, Learning 51.3 Overview and roadmap 61.3.1 Overview of Chapters 61.3.2Readers guide1.3.3 Connection to Other Disciplines1.4 Historical notes 122 Foundations2. 1 Probability Theory2. 1. 1 Probability Distributions 152. 1.2 Basic Concepts in Probability 182. 1.3 Random Variables and Joint Distributions 192. 1.4 Independence and Conditional Independence 2:2. 1.5 Querying a Distribution2. 1.6 Continuous Spaces 272. 1.7 Expectation and Variance 312.2 Graphs 342.2.1Nodes and edges 342.2.2 Subgraphs 352.2.3 Paths and trails 36
Adaptive Computation and Machine LearningThomas dietterich, EditorChristopher Bishop, David Heckerman, Michael Jordan, and Michael Kearns, Associate EditorsBioinformatics: The Machine learning Approach, Pierre Baldi and Soren BrunakReinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. BartoGraphical models for Machine Learning and Digital Communication, Brendan J. FreyLearning in graphical Models, Michael I. JordanCausation, Prediction, and Search, 2nd ed, Peter Spirtes, Clark Glymour, and Richard ScheinesPrinciples of Data Mining, David Hand, Heikki Mannila, and Padhraic SmythBioinformatics: The Machine Learning Approach, 2nd ed, Pierre Baldi and Soren BrunakLearning Kernel classifiers: Theory and Algorithms, Ralf HerbrichLearning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, Bernhard Scholkopf and Alexander J smolaIntroduction to Machine Learning, Ethem AlpaydinGaussian Processes for Machine Learning, Carl Edward Rasmussen and Christopher K.I. WilliamsSemi-Supervised Learning, Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien, edsThe Minimum description Length Principle, Peter D GrunwaldIntroduction to Statistical Relational Learning, lise Getoor and Ben Taskar, edsProbabilistic Graphical Models: Principles and Techniques, Daphne Koller and Nir FriedmanProbabilistic Graphical ModelsPrinciples and TechniquesDaphne kollerNir friedmanThe mit pressCambridge, MassachusettsLondon, England@2009 Massachusetts Institute of TechnologyAll rights reserved. No part of this book may be reproduced in any form by any electronicor mechanical means (including photocopying, recording, or information storage and retrieval)without permission in writing from the publisherFor information about special quantity discounts, please email special_sales@mitpress. mit. eduThis book was set by the authors in BlFX2EPrinted and bound in the united states of americaLibrary of Congress Cataloging-in-Publication DataKoller, DaphneProbabilistic Graphical Models: Principles and Techniques Daphne Koller and Nir Friedmanp cm. -(Adaptive computation and machine learning)Includes bibliographical references and indexisBn 978-0-262-01319-2(hardcover: alk. paper1. Graphical modeling(Statistics) 2. Bayesian statistical decision theory--Graphic methods. IKoller, Daphne. II. Friedman, NirQA279.5.K652010519.5’420285-dc222009008615109876543To our familiesmy parents Dov and ditzamy husband danmy daughters natalie and mayaDKmy parents Noga and Gadmy wifemy children roy and liorMEAs far as the laws of mathematics refer to reality, they are not certain, as far as they arecertain, they do not refer to realityAlbert einstein 1956When we try to pick out anything by itself, we find that it is bound fast by a thousandinvisible cords that cannot be broken, to everything in the universeJohn Muir, 1869The actual science of logic is conversant at present only with things either certain, impossible,or entirely doubtful . Therefore the true logic for this world is the calculus of probabilities,which takes account of the magnitude of the probability which is, or ought to be, in areasonable man's mindJames Clerk Maxwell, 1850The theory of probabilities is at bottom nothing but common sense reduced to calculus;itenables us to appreciate with exactness that which accurate minds feel with a sort of instinctfor which ofttimes they are unable to account.Pierre Simon Laplace, 1819Misunderstanding of probability may be the greatest of all impediments to scientific literacyStephen Jay GouldContentsAcknowledgmentsList of figuresList of algorithmsList of boxesXXX1 IntroductionL1 Motivation 11.2 Structured Probabilistic Models 21.2.1 Probabilistic Graphical Models 31.2.2 Representation, Inference, Learning 51.3 Overview and roadmap 61.3.1 Overview of Chapters 61.3.2Readers guide1.3.3 Connection to Other Disciplines1.4 Historical notes 122 Foundations2. 1 Probability Theory2. 1. 1 Probability Distributions 152. 1.2 Basic Concepts in Probability 182. 1.3 Random Variables and Joint Distributions 192. 1.4 Independence and Conditional Independence 2:2. 1.5 Querying a Distribution2. 1.6 Continuous Spaces 272. 1.7 Expectation and Variance 312.2 Graphs 342.2.1Nodes and edges 342.2.2 Subgraphs 352.2.3 Paths and trails 36
2019-12-21 22:22:21
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PGMView是一款专业实用的PGM图像查看工具。该款工具主要用于查看便携式灰色地图格式的图像，软件操作简单，仅适用于pgm图像的查看，无法进行编辑，欢迎下载
PGM图像介绍
PGM 是便携式灰度图像格式(portable graymap file format),在黑白超声图像系统中经常使用PGM格式的图像.文件的后缀名为".pgm",PGM格式图像格式分为两类:P2和P5类型.不管是P2还
2019-12-21 21:40:40
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《概率图模型学习理论及其应用》；赵悦著；清华大学出版社；157页
概率图模型领域为数不多的国内好教材，一百多页，绝对平易近人（吐槽：Koller的经典著作，那厚度，像板儿砖，还没看就能吓死你）
2019-12-21 21:21:06
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图像处理最为经典的lena图片，包含彩色(tiff格式)，灰度（bmp,mat,pgm格式），图片大小512*512
2019-12-21 20:43:21
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