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A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: state-of-the-art algorithms, a taxonomy of the field, applications, benchmark experiments, and directions for future research. In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.
(source: Nielsen Book Data)

• Intro; SEMI-SUPERVISED LEARNINGBACKGROUND, APPLICATIONSAND FUTURE DIRECTIONS; SEMI-SUPERVISED LEARNINGBACKGROUND, APPLICATIONSAND FUTURE DIRECTIONS; CONTENTS; PREFACE; Introduction to This Book; Target Audience; Acknowledgments; Chapter 1CONSTRAINED DATASELF-REPRESENTATIVE GRAPHCONSTRUCTION; Abstract;
• 1. Introduction;
• 2. Constrained Data Self-Representative GraphConstruction;
• 3. Kernelized Variants; 3
• .1. Hilbert Space; 3
• .2. Column Generation;
• 4. Performance Evaluation; 4
• .1. Label Propagation; 4.1
• .1. Gaussian Random Fields; 4.1
• .2. Local and Global Consistency; 4
• .2. Experimental Results

• 4.2
• .1. Comparison among Several Graph Construction Methods4.2
• .2. Stability of the Proposed Method; 4.2
• .3. Sensitivity to Parameters; 4.2
• .4. Computational Complexity and CPU Time; Acknowledgments; Conclusion; References; Chapter 2INJECTING RANDOMNESS INTO GRAPHS:AN ENSEMBLE SEMI-SUPERVISEDLEARNING FRAMEWORK; Abstract;
• 1. Introduction;
• 2. Background; 2
• .1. Graph-Based Semi-Supervised Learning; 2
• .2. Ensemble Learning and Random Forests; 2
• .3. Anchor Graph;
• 3. Random Multi-Graphs; 3
• .1. Problem Formulation; 3
• .2. Algorithm; 3
• .3. Graph Construction; 3
• .4. Semi-Supervised Inference

• 3
• .5. Inductive Extension3
• .6. Randomness as Regularization;
• 4. Experiments; 4
• .1. Data Sets; 4
• .2. Experimental Results; 4
• .3. Impact of Parameters; 4
• .4. Hyperspectral Image Classification; Acknowledgments; Conclusion; References; Chapter 3LABEL PROPAGATION VIA KERNELFLEXIBLE MANIFOLD EMBEDDING; Abstract;
• 1. Introduction;
• 2. RelatedWork; 2
• .1. Semi-Supervised Discriminant Analysis; 2
• .2. Semi-Supervised Discriminant Embedding; 2
• .3. Laplacian Regularized Least Square; 2
• .4. Review of the Flexible Manifold Embedding Framework;
• 3. Kernel FlexibleManifold Embedding; 3
• .1. The Objective Function

• 3
• .2. Optimal Solution3
• .3. The Algorithm; 3
• .4. Difference between KFME and Existing Methods; 3.4
• .1. Difference between KFME and FME; 3.4
• .2. Difference between KFME and Other Methods;
• 4. Experimental Results; 4
• .1. Datasets; 4
• .2. Method Comparison; 4
• .3. Results Analysis; 4
• .4. Stability with Respect to Graph; Acknowledgments; Conclusion; References; Chapter 4FAST GRAPH-BASED SEMI-SUPERVISEDLEARNING AND ITS APPLICATIONS; Abstract;
• 1. Introduction;
• 2. Related Work; 2
• .1. Scalable Graph-Based SSL/TL Methods; 2
• .2. Scalable Graph Construction Methods; 2
• .3. Robust Graph-Based SSL/TL Methods

• 3. Minimum Tree Cut Method3
• .1. Notations; 3
• .2. The Proposed Method; 3
• .3. The Tree Labeling Algorithm; 3
• .4. Generate a Spanning Tree from a Graph;
• 4. Insensitiveness to Graph Construction;
• 5. Experiments; 5
• .1. Data Set; 5.1
• .1. UCI Data Set; 5.1
• .2. Image; 5.1
• .3. Text; 5
• .2. Graph Construction; 5
• .3. Accuracy; 5
• .4. Speed; 5
• .5. Robustness; 5
• .6. Effect of Different Spanning Tree and Ensemble of MultipleSpanning Trees;
• 6. Applications in Text Extraction; 6
• .1. Interactive Text Extraction in Natural Scene Images; 6
• .2. Document Image Binarization; Conclusion and FutureWork; References

• Introduction Graph Construction Learning and Inference Scalability Applications Future Work Bibliography Authors' Biographies Index.
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While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer vision, natural language processing, and other areas of Artificial Intelligence. Recognizing this promising and emerging area of research, this synthesis lecture focuses on graph-based SSL algorithms (e.g., label propagation methods). Our hope is that after reading this book, the reader will walk away with the following: (1) an in-depth knowledge of the current state-of-the-art in graph-based SSL algorithms, and the ability to implement them; (2) the ability to decide on the suitability of graph-based SSL methods for a problem; and (3) familiarity with different applications where graph-based SSL methods have been successfully applied.
(source: Nielsen Book Data)

• Introduction to Statistical Machine Learning Overview of Semi-Supervised Learning Mixture Models and EM Co-Training Graph-Based Semi-Supervised Learning Semi-Supervised Support Vector Machines Human Semi-Supervised Learning Theory and Outlook.
• (source: Nielsen Book Data)

Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field.
(source: Nielsen Book Data)

Miguel Morales, the master of RL domain and the author of "Grokking Deep Reinforcement Learning", demonstrates how to make reinforcement learning more like supervised learning with the help of the popular algorithm Deep Q-Network, which is still one of the best performing DRL agents.

• 1. Prologue: linking 'The Future' with the present
• 2. A data assimilation reminder
• 3. Remembrance of things path
• 4. SDA variational principles
• Euler-Lagrange equations and Hamiltonian formulation
• 5. Using waveform information
• 6. Annealing in the model precision Rf
• 7. Discrete time integration in data assimilation variational principles
• Lagrangian and Hamiltonian formulations
• 8. Monte Carlo methods
• 9. Machine learning and its equivalence to statistical data assimilation
• 10. Two examples of the practical use of data assimilation
• 11. Unfinished business
• Bibliography
• Index.
• (source: Nielsen Book Data)

Data assimilation is a hugely important mathematical technique, relevant in fields as diverse as geophysics, data science, and neuroscience. This modern book provides an authoritative treatment of the field as it relates to several scientific disciplines, with a particular emphasis on recent developments from machine learning and its role in the optimisation of data assimilation. Underlying theory from statistical physics, such as path integrals and Monte Carlo methods, are developed in the text as a basis for data assimilation, and the author then explores examples from current multidisciplinary research such as the modelling of shallow water systems, ocean dynamics, and neuronal dynamics in the avian brain. The theory of data assimilation and machine learning is introduced in an accessible and unified manner, and the book is suitable for undergraduate and graduate students from science and engineering without specialized experience of statistical physics.
(source: Nielsen Book Data)

• Introduction Supervised and Unsupervised Prediction Semi-Supervised Learning Learning under Bias Learning under Unknown Bias Evaluating under Bias.
• (source: Nielsen Book Data)

This book introduces basic supervised learning algorithms applicable to natural language processing (NLP) and shows how the performance of these algorithms can often be improved by exploiting the marginal distribution of large amounts of unlabeled data. One reason for that is data sparsity, i.e., the limited amounts of data we have available in NLP. However, in most real-world NLP applications our labeled data is also heavily biased. This book introduces extensions of supervised learning algorithms to cope with data sparsity and different kinds of sampling bias.This book is intended to be both readable by first-year students and interesting to the expert audience. My intention was to introduce what is necessary to appreciate the major challenges we face in contemporary NLP related to data sparsity and sampling bias, without wasting too much time on details about supervised learning algorithms or particular NLP applications. I use text classification, part-of-speech tagging, and dependency parsing as running examples, and limit myself to a small set of cardinal learning algorithms. I have worried less about theoretical guarantees ("this algorithm never does too badly") than about useful rules of thumb ("in this case this algorithm may perform really well"). In NLP, data is so noisy, biased, and non-stationary that few theoretical guarantees can be established and we are typically left with our gut feelings and a catalogue of crazy ideas. I hope this book will provide its readers with both. Throughout the book we include snippets of Python code and empirical evaluations, when relevant.
(source: Nielsen Book Data)

• Automating Inquiry Uncertainty Sampling Searching Through the Hypothesis Space Minimizing Expected Error and Variance Exploiting Structure in Data Theory Practical Considerations.
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The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose ""queries, "" usually in the form of unlabeled data instances to be labeled by an ""oracle"" (e.g., a human annotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain. This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or ""query selection frameworks."" We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Overview of supervised learning
• 3. Linear methods for regression
• 4. Linear methods for classification
• 5. Basis expansions and regularization
• 6. Kernel smoothing methods
• 7. Model assessment and selection
• 8. Model inference and averaging
• 9. Additive models, trees, and related methods
• 10. Boosting and additive trees
• 11. Neural networks
• 12. Support vector machines and flexible discriminants
• 13. Prototype methods and nearest-neighbors
• 14. Unsupervised learning
• 15. Random forests
• 16. Ensemble learning
• 17. Undirected graphical models
• 18. High-dimensional problems: p>> N.

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates.
(source: Nielsen Book Data)