Computer Age Statistical Inference: Algorithms，Evidence，and Data Science.

1 Algorithms and Inference 3 1.1 A Regression Example 4 1.2 Hypothesis Testing 8 1.3 Notes 11 2 Frequentist Inference 12 2.1 Frequentism in Practice 14 2.2 Frequentist Optimality 18 2.3 Notes and Details 20 3 Bayesian Inference 22 3.1 Two Examples 24 3.2 Uninformative Prior Distributions 28 3.3 Flaws in Frequentist Inference 30 3.4 A Bayesian/Frequentist Comparison List 33 3.5 Notes and Details 36 4 Fisherian Inference and Maximum Likelihood Estimation 38 4.1 Likelihood and Maximum Likelihood 38 4.2 Fisher Information and the MLE 41 4.3 Conditional Inference 45 4.4 Permutation and Randomization 49 4.5 Notes and Details 51 5 Parametric Models and Exponential Families 53 ix x Contents 5.1 Univariate Families 54 5.2 The Multivariate Normal Distribution 55 5.3 Fisher’s Information Bound for Multiparameter Families 59 5.4 The Multinomial Distribution 61 5.5 Exponential Families 64 5.6 Notes and Details 69 Part II Early Computer-Age Methods 73 6 Empirical Bayes 75 6.1 Robbins’ Formula 75 6.2 The Missing-Species Problem 78 6.3 A Medical Example 84 6.4 Indirect Evidence 1 88 6.5 Notes and Details 88 7 James–Stein Estimation and Ridge Regression 91 7.1 The James–Stein Estimator 91 7.2 The Baseball Players 94 7.3 Ridge Regression 97 7.4 Indirect Evidence 2 102 7.5 Notes and Details 104 8 Generalized Linear Models and Regression Trees 108 8.1 Logistic Regression 109 8.2 Generalized Linear Models 116 8.3 Poisson Regression 120 8.4 Regression Trees 124 8.5 Notes and Details 128 9 Survival Analysis and the EM Algorithm 131 9.1 Life Tables and Hazard Rates 131 9.2 Censored Data and the Kaplan–Meier Estimate 134 9.3 The Log-Rank Test 139 9.4 The Proportional Hazards Model 143 9.5 Missing Data and the EM Algorithm 146 9.6 Notes and Details 150 10 The Jackknife and the Bootstrap 155 10.1 The Jackknife Estimate of Standard Error 156 10.2 The Nonparametric Bootstrap 159 10.3 Resampling Plans 162 Contents xi 10.4 The Parametric Bootstrap 169 10.5 Influence Functions and Robust Estimation 174 10.6 Notes and Details 177 11 Bootstrap Confidence Intervals 181 11.1 Neyman’s Construction for One-Parameter Problems 181 11.2 The Percentile Method 185 11.3 Bias-Corrected Confidence Intervals 190 11.4 Second-Order Accuracy 192 11.5 Bootstrap-t Intervals 195 11.6 Objective Bayes Intervals and the Confidence Distribution 198 11.7 Notes and Details 204 12 Cross-Validation and Cp Estimates of Prediction Error 208 12.1 Prediction Rules 208 12.2 Cross-Validation 213 12.3 Covariance Penalties 218 12.4 Training, Validation, and Ephemeral Predictors 227 12.5 Notes and Details 230 13 Objective Bayes Inference and MCMC 233 13.1 Objective Prior Distributions 234 13.2 Conjugate Prior Distributions 237 13.3 Model Selection and the Bayesian Information Criterion 243 13.4 Gibbs Sampling and MCMC 251 13.5 Example: Modeling Population Admixture 256 13.6 Notes and Details 261 14 Postwar Statistical Inference and Methodology 264 Part III Twenty-First-Century Topics 269 15 Large-Scale Hypothesis Testing and FDRs 271 15.1 Large-Scale Testing 272 15.2 False-Discovery Rates 275 15.3 Empirical Bayes Large-Scale Testing 278 15.4 Local False-Discovery Rates 282 15.5 Choice of the Null Distribution 286 15.6 Relevance 290 15.7 Notes and Details 294 16 Sparse Modeling and the Lasso 298 xii Contents 16.1 Forward Stepwise Regression 299 16.2 The Lasso 303 16.3 Fitting Lasso Models 308 16.4 Least-Angle Regression 309 16.5 Fitting Generalized Lasso Models 313 16.6 Post-Selection Inference for the Lasso 317 16.7 Connections and Extensions 319 16.8 Notes and Details 321 17 Random Forests and Boosting 324 17.1 Random Forests 325 17.2 Boosting with Squared-Error Loss 333 17.3 Gradient Boosting 338 17.4 Adaboost: the Original Boosting Algorithm 341 17.5 Connections and Extensions 345 17.6 Notes and Details 347 18 Neural Networks and Deep Learning 351 18.1 Neural Networks and the Handwritten Digit Problem 353 18.2 Fitting a Neural Network 356 18.3 Autoencoders 362 18.4 Deep Learning 364 18.5 Learning a Deep Network 368 18.6 Notes and Details 371 19 Support-Vector Machines and Kernel Methods 375 19.1 Optimal Separating Hyperplane 376 19.2 Soft-Margin Classifier 378 19.3 SVM Criterion as Loss Plus Penalty 379 19.4 Computations and the Kernel Trick 381 19.5 Function Fitting Using Kernels 384 19.6 Example: String Kernels for Protein Classification 385 19.7 SVMs: Concluding Remarks 387 19.8 Kernel Smoothing and Local Regression 387 19.9 Notes and Details 390 20 Inference After Model Selection 394 20.1 Simultaneous Confidence Intervals 395 20.2 Accuracy After Model Selection 402 20.3 Selection Bias 408 20.4 Combined Bayes–Frequentist Estimation 412 20.5 Notes and Details 417 Contents xiii 21 Empirical Bayes Estimation Strategies 421 21.1 Bayes Deconvolution 421 21.2 g-Modeling and Estimation 424 21.3 Likelihood, Regularization, and Accuracy 427 21.4 Two Examples 432 21.5 Generalized Linear Mixed Models 437 21.6 Deconvolution and f -Modeling 440 21.7 Notes and Details 444 Epilogue 446 References 453 Author Index 463 Subject Index 467

Data Mining-Practical Machine Learning Tools and Techniques-4ed-2017

Deep learning .................................................. 417 10.1 Deep Feedforward Networks ...................................................420 The MNIST Evaluation ........................................................... 421 Losses and Regularization ....................................................... 422 Deep Layered Network Architecture ...................................... 423 Activation Functions................................................................ 424 Backpropagation Revisited...................................................... 426 Computation Graphs and Complex Network Structures ........ 429 Checking Backpropagation Implementations ......................... 430 10.2 Training and Evaluating Deep Networks ................................431 Early Stopping ......................................................................... 431 Validation, Cross-Validation, and Hyperparameter Tuning ... 432 Mini-Batch-Based Stochastic Gradient Descent ..................... 433 Pseudocode for Mini-Batch Based Stochastic Gradient Descent.................................................................................434 Learning Rates and Schedules................................................. 434 Regularization With Priors on Parameters.............................. 435 Dropout .................................................................................... 436 Batch Normalization................................................................ 436 Parameter Initialization............................................................ 436 Unsupervised Pretraining......................................................... 437 Data Augmentation and Synthetic Transformations............... 437 10.3 Convolutional Neural Networks ..............................................437 The ImageNet Evaluation and Very Deep Convolutional Networks ..............................................................................438 From Image Filtering to Learnable Convolutional Layers..... 439 Convolutional Layers and Gradients....................................... 443 Pooling and Subsampling Layers and Gradients .................... 444 Implementation ........................................................................ 445 10.4 Autoencoders............................................................................445 Pretraining Deep Autoencoders With RBMs.......................... 448 Denoising Autoencoders and Layerwise Training.................. 448 Combining Reconstructive and Discriminative Learning....... 449 xii Contents

Super Resolution of Images and Video

视频图像超分辨率的一本书籍，主要内容有： Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 What is Super Resolution of Images and Video? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Why and When is Super Resolution Possible? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.4 Book Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2. Bayesian Formulation of Super-Resolution Image Reconstruction . . . . . . . . . . . . . . . 13 2.1 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2 Bayesian Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.3 Bayesian Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.4 Hierarchical Bayesian Modeling and Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3. Low-Resolution Image Formation Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.1 Image Formation Models for Uncompressed Observations . . . . . . . . . . . . . . . . . . 19 3.1.1 The Warp–Blur Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.1.2 The Blur–Warp Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2 Image Formation Models for Compressed Observations . . . . . . . . . . . . . . . . . . . . 27 3.3 Limits on Super Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4. Motion Estimation in Super Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.1 Motion Estimation from Uncompressed Observations . . . . . . . . . . . . . . . . . . . . . . 39 4.2 Motion Estimation from Compressed Observations . . . . . . . . . . . . . . . . . . . . . . . . 45 4.3 How to Detect Unreliable Motion Estimates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .49 4.4 Consistency of Motion Estimates for Super Resolution . . . . . . . . . . . . . . . . . . . . . 51 4.5 Some Open Issues in Motion Estimation for Super Resolution . . . . . . . . . . . . . . 55 5. Estimation of High-Resolution Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 5.1 High-Resolution Image Estimation from Uncompressed Sequences . . . . . . . . . . 58 5.2 High-Resolution Image Estimation from Compressed Sequences . . . . . . . . . . . . 71 5.3 Some Open Issues in Image Estimation for Super Resolution . . . . . . . . . . . . . . . . 75 6. Bayesian Inference Models in Super Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 6.1 Hierarchical Bayesian Framework for Super Resolution . . . . . . . . . . . . . . . . . . . . . 78 6.2 Inference Models for Super Resolution Reconstruction Problems . . . . . . . . . . . . 79 6.3 Some Open Issues in Super Resolution Bayesian Inference . . . . . . . . . . . . . . . . . . 89 7. Super Resolution for Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 7.1 Pre- and Post-Processing of Video Sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 7.2 Including Super Resolution into the Compression Scheme . . . . . . . . . . . . . . . . . . 94 7.2.1 Region-Based Super Resolution for Compression . . . . . . . . . . . . . . . . . . . 98 7.2.1.1 Motion and Texture Segmentation . . . . . . . . . . . . . . . . . . . . . . . 100 7.2.1.2 Downsampling Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 7.2.1.3 Upsampling Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

Introduction to Machine Learning (Second Edition)

1 Introduction 1 2 Supervised Learning 21 3 Bayesian Decision Theory 47 4 Parametric Methods 61 5 Multivariate Methods 87 6 Dimensionality Reduction 109 7 Clustering 143 8 Nonparametric Methods 163 9 Decision Trees 185 10 Linear Discrimination 209 11 Multilayer Perceptrons 233 12 Local Models 279 13 Kernel Machines 309 14 Bayesian Estimation 341 15 Hidden Markov Models 363 16 Graphical Models 387 17 Combining Multiple Learners 419 18 Reinforcement Learning 447 19 Design and Analysis of Machine Learning Experiments 475 A Probability

Bayesian Reasoning and Machine Learning

1: Probabilistic Reasoning 2: Basic Graph Concepts 3: Belief Networks 4: Graphical Models 5: Efficient Inference in Trees 6: The Junction Tree Algorithm 7: Making Decisions 8: Statistics for Machine Learning 9: Learning as Inference 10: Naive Bayes 11: Learning with Hidden Variables 12: Bayesian Model Selection 13: Machine Learning Concepts 14: Nearest Neighbour Classification 15: Unsupervised Linear Dimension Reduction 16: Supervised Linear Dimension Reduction 17: Linear Models 18: Bayesian Linear Models 19: Gaussian Processes 20: Mixture Models 21: Latent Linear Models 22: Latent Ability Models 23: Discrete-State Markov Models 24: Continuous-State Markov Models 25: Switching Linear Dynamical Systems 26: Distributed Computation 27: Sampling 28: Deterministic Approximate Inference

Learning with support vector machines

Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi 1 Support Vector Machines for Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Support Vector Machines for binary classification . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Multi-class classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.4 Learning with noise: soft margins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.5 Algorithmic implementation of Support Vector Machines . . . . . . . . . . . . . . . . . . . 14 1.6 Case Study 1: training a Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1.7 Case Study 2: predicting disease progression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 1.8 Case Study 3: drug discovery through active learning . . . . . . . . . . . . . . . . . . . . . . . . 21 2 Kernel-based Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.2 Other kernel-based learning machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.3 Introducing a confidence measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.4 One class classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.5 Regression: learning with real-valued labels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.6 Structured output learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3 Learning with Kernels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.2 Properties of kernels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.3 Simple kernels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.4 Kernels for strings and sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.5 Kernels for graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.6 Multiple kernel learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3.7 Learning kernel combinations via a maximum margin approach . . . . . . . . . . . . . . 57 3.8 Algorithmic approaches to multiple kernel learning . . . . . . . . . . . . . . . . . . . . . . . . . 59 3.9 Case Study 4: protein fold prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62viii A Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 A.1 Introduction to optimization theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 A.2 Duality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 A.3 Constrained optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

小波分析与现代科学_新版本 ppt

小波和小波变换 小波分析和时-频分析 正交小波和多尺度分析 多分辨分析和金字塔算法 Malvar小波 (Malvar’s Wavelet) 小波包(Wavelet Packets) 总结和展望

MIT课件 凸分析与优化

MIT 凸分析与优化方面的课件， 中文版的。

图像处理和分析-变分，偏微分方程，小波及随机方法（Image processing and analysis - variational, pde, wavelet and stochastic methods）

1 Introduction 1.1 Dawning of the Era of Imaging Sciences 1.1.1 Image Acquisition 1.1.2 Image Processing 1.1.3 Image Interpretation and Visual Intelligence 1.2 Image Processing by Examples 1.2.1 Image Contrast Enhancement 1.2.2 Image Denoisirg 1.2.3 Image Deblurring 1.2.4 Image Inpainting 1.2.5 Image Segmentation 1.3 An Overview of Methodologies in Image Processing 1.3.1 Morphological Approach 1.3.2 Fourier and Spectral Analysis 1.3.3 Wavelet and Space-Scale Analysis 1.3.4 Stochastic Modeling 1.3.5 Variaticnal Methods 1.3.6 Partial Differential Equations （PDEs） 1.3.7 Different Approaches Are Intrinsically Interconnected 2d6 1.4 Organization of the Book 1.5 How to Read the Bcok 2 Some Modern Image Analysis Tools 2.1 Geometry of Curves and Surfaces 2.1.I Geometry of Curves 2.1.2 Geometry of Surfaces in Three Dimensions 2.1.3 Hausdorff Measures and Dimensions 2.2 Functions with Bounded Variations 2.2.1 Total Variatien as a Radon Measure 2.2.2 Basic Properties of BV Functions 2.2.3 The Co-Area Formula 2.3 Elements of Thermodynamics and Statistical Mechanics 2.3.1 Essentials of Thermodynamics 2.3.2 Entropy and Potentials 2.3.3 Statistical Mechanics of Ensembles 2.4 Bayesian Statistical Inference 2.4.1 Image Processing or Visual Perception as Inference 2.4.2 Bayesian Inference： Bias 2d6 Due to Prior Knowledge 2.4.3 Bayesian Method in Image Processing 2.5 Linear and Nonlinear Filtering and Diffusion 2.5.1 Point Spreading and Markov Transition 2.5.2 Linear Filtering and Diffusion 2.5.3 Nonlinear Filtering and Diffusion 2.6 Wavelets and Multiresolution Analysis 2.6.1 Quest for New Image Analysis Tools 2.6.2 Early Edge Theory and Marr’s Wavelets 2.6.3 Windowed Frequency Analysis and Gabor Wavelets 2.6.4 Frequency-Window Coupling： Malvar-Wilson Wavelets 2.6.5 The Framework of Multiresolution Analysis （MRA） 2.6.6 Fast Image Analysis and Synthesis via Filter Banks 3 Image Modeling and Representation 3.1 Modeling and Representation： What, Why, and How 5b4 3.2 Deterministic Image Models 3.2.1 Images as Distributions （Generalized Functions） 3.2.2 Lp Images 3.2.3 Sobolev Images Hn（Ω） 3.2.4 BV Images 3.3 Wavelets and Multiscale Representation 3.3.1 Construction of 2-D Wavelets 3.3.2 Wavelet Responses to Typical Image Features 3.3.3 Besov Images and Sparse Wavelet Representation 3.4 Lattice and Random Field Representation 3.4.1 Natural Images of Mother Nature 3.4.2 Images as Ensembles and Distributions 3.4.3 Images as Gibbs’ Ensembles 3.4.4 Images as Markov Random Fields 3.4.5 Visual Filters and Filter Banks 3.4.6 Entropy-Based Learning of Image Patterns 3.5 Level-Set Representation 3.5.1 Classical Level Sets 3.5.2 Cumulative Level Sets 3.5.3 Level-Set Synthesis 3.5.4 An Example： Level Sets of Piecewise Constant Images 3.5.5 High Order Regularity of Level Sets 3.5.6 Statistics of Level Sets of Natural Images 3.6 The Mumford-Shah Free Boundary Image Model 3.6.1 Piecewise Constant 1-D Images: Analysis and Synthesis 3.6.2 Piecewise Smooth 1-D Images: First Order Representation 3.6.3 Piecewise Smooth I-D Images: Poisson Representation 3.6.4 Piecewise Smooth 2-D Images 3.6.5 The Mumford-Shah Model 3.6.6 The Role of Special B V Images 4 Image Denoising 4. 1 Noise: Origins. Physics. and Models 4.l. 1 Origins and Physics of Noise 4.1.2 A Brief Overview of 1-D Stochastic Signals 5b4 4.1.3 Stochastic Models of Noises 4.1.4 Analog White Noises as Random Generalized Functions 4.1.5 Random Signals from Stochastic Differential Equations 4.l.6 2-D Stochastic Spatial Signals: Random Fields 4.2 Linear Denoising: Lowpass Filtering 4.2.1 Signal vs. Noise 4.2.2 Denoising via Linear Filters and Diffusion 4.3 Data-Driven Optimal Filtering: Wiener Filters 4.4 Wavelet Shrinkage Denoising 4.4.1 Shrinkage: Quasi-statistical Estimation of Singletons 4.4.2 Shrinkage: Variational Estimation of Singletons 4.4.3 Denoising via Shrinking Noisy Wavelet Components 4.4.4 Variational Denoising of Noisy Besov Images 4.5 Variational Denoising Based on BV Image Model 4.5.1 TV. Robust Statistics. and Median 4.5.2 The Role of TV and BV Image Model 4.5.3 Biased Iterated Median Filtering 4.5.4 Rudin. Osher. and Fatemi s TV Denoising Model 4.5.5 Computational Approaches to TV Denoising 4.5.6 Duality for the TV Denoising Model 4.5.7 Solution Structures of the TV Denoising Model 4.6 Denoising via Nonlinear Diffusion and Scale-Space Theory 4.6.1 Perona and Malik s Nonlinear Diffusion Model 4.6.2 Axiomatic Scale-Space Theory 4.7 Denoising Salt-and-Pepper Noise 4.8 Multichannel TV Denoising 4.8.1 Variational TV Denoising of Multichannel Images 4.8.2 Three Versions of TV 5 Image Deblurring 5.1 Blur: Physical Origins and Mathematical Models 5.1.1 Physical Origi 5b4 ns 5.1.2 Mathematical Models of Blurs 5.1.3 Linear vs. Nonlinear Blurs 5.2 Ill-posedness and Regularization 5.3 Deblurring with Wiener Filters 5.3.1 Intuition on Filter-Based Deblurring 5.3.2 Wiener Filtering 5.4 Deblurring of BV Images with Known PSF 5.4.1 The Variational Model 5.4.2 Existence and Uniqueness 5.4.3 Computation 5.5 Variational Blind Deblurring with Unknown PSF 5.5.1 Parametric Blind Deblurring 5.5.2 Parametric-Field-Based Blind Deblurring 5.5.3 Nonparametric Blind Deblurring 6 Image Inpainting 6.1 A Brief Review on Classical Interpolation Schemes 6.1.1 Polynomial Interpolation 6.1.2 Trigonometric Polynomial Interpolation 6.1.3 Spline Interpolation 6.1.4 Shannon s Sampling Theorem 6.1.5 Radial Basis Functions and Thin-Plate Splines 6.2 Challenges and Guidelines for 2-D Image Inpainting 6.2.1 Main Challenges for Image Inpainting 6.2.2 General Guidelines for Image Inpainting 6.3 Inpainting of Sobolev Images: Green s Formulae 6.4 Geometric Modeling of Curves and Images 6.4.1 Geometric Curve Models 6.4.2 2-. 3-Point Accumulative Energies. Length. and Curvature. 6.4.3 Image Models via Functionalizing Curve Models 6.4.4 Image Models with Embedded Edge Models 6.5 Inpainting BV Images (via the TV Radon Measure) 6.5.1 Formulation of the TV Inpainting Model 6.5.2 Justification of TV Inpainting by Visual Perception 6.5.3 Computa 5b4 tion of TV lnpainting 6.5.4 Digital Zooming Based on TV Inpainting 6.5.5 Edge-Based Image Coding via Inpainting 6.5.6 More Examples and Applications of TV Inpainting 6.6 Error Analysis for Image Inpainting 6.7 Inpainting Piecewise Smooth Images via Mumford and Shah 6.8 Image Inpainting via Euler s Elasticas and Curvatures 6.8.1 Inpainting Based on the Elastica Image Model 6.8.2 Inpainting via Mumford-Shah-Euler Image Model 6.9 Inpainting of Meyer s Texture 6.10 Image Inpainting with Missing Wavelet Coefficients 6.11 PDE Inpainting: Transport. Diffusion. and Navier-Stokes 6.11.1 Second Order Interpolation Models 6.11.2 A Third Order PDE Inpainting Model and Navier-Stokes …… 需要安装 djvu阅读器

pattern recognition and machine learning（模式识别和机器学习）习题答案

C.M.Bishop （微软剑桥研究员副主管） 的经典之作的习题解答。主要内容包含 线性回归和分类模型，神经网络，核方法，稀疏核方法， 图模型， 逼近推理，采样方法和连续隐模型。