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• Introduction.- Overview of supervised learning.- Linear methods for regression.- Linear methods for classification.- Basis expansions and regularization.- Kernel smoothing methods.- Model assessment and selection.- Model inference and averaging.- Additive models, trees, and related methods.- Boosting and additive trees.- Neural networks.- Support vector machines and flexible discriminants.- Prototype methods and nearest-neighbors.- Unsupervised learning.
• (source: Nielsen Book Data)

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)

• Overview of Supervised Learning.- Linear Methods for Regression.- Linear Methods for Classification.- Basic Expansions and Regularization.- Kernel Methods.- Model Assessment and Selection.- Model Inference and Averaging.- Additive Models, Trees, and Related Methods.- Boosting and Additive Trees.- Neural Networks.- Support Vector Machines and Flexible Discriminates.- Prototype Methods and Nearest Neighbors.- Unsupervised Learning.
• (source: Nielsen Book Data)

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas 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 color graphics. It should be 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 and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering.There is also a chapter on methods for 'wide' data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful "An Introduction to the Bootstrap". Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.
(source: Nielsen Book Data)

• Overview of Supervised Learning.- Linear Methods for Regression.- Linear Methods for Classification.- Basic Expansions and Regularization.- Kernel Methods.- Model Assessment and Selection.- Model Inference and Averaging.- Additive Models, Trees, and Related Methods.- Boosting and Additive Trees.- Neural Networks.- Support Vector Machines and Flexible Discriminates.- Prototype Methods and Nearest Neighbors.- Unsupervised Learning.
• (source: Nielsen Book Data)

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas 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 color graphics. It should be 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 and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering.There is also a chapter on methods for 'wide' data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful "An Introduction to the Bootstrap". Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.
(source: Nielsen Book Data)

During the past decade there has been an explosion in computation and information technology.; With it has come a vast amount of data in a variety of fields such as medicine, biology, finance, and marketing.; The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics.; Many of these tools have common underpinnings but are often expressed with different terminology.; This book describes the important ideas in these areas in a common conceptual framework.; While the approach is statistical, the emphasis is on concepts rather than mathematics.

• International Workshop on Continual Semi-Supervised Learning: Introduction, Benchmarks and Baselines.- Unsupervised Continual Learning Via Pseudo Labels.- Transfer and Continual Supervised Learning for Robotic Grasping through Grasping Features.- Unsupervised Continual Learning via Self-Adaptive Deep Clustering Approach.- Evaluating Continual Learning Algorithms by Generating 3D Virtual Environments.- A Benchmark and Empirical Analysis for Replay Methods in Continual Learning.- SPeCiaL: Self-Supervised Pretraining for Continual Learning.- Distilled Replay: Overcoming Forgetting through Synthetic Samples.- Self-supervised Novelty Detection for Continual Learning: A Gradient-based Approach Boosted by Binary Classification.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the First International Workshop on Continual Semi-Supervised Learning, CSSL 2021, which took place as a virtual event during August 2021.The 9 full papers and 0 short papers included in this book were carefully reviewed and selected from 14 submissions.
(source: Nielsen Book Data)

• - 1. The Case for Quantification.
• 2. Applications of Quantification.
• 3. Evaluation of Quantification Algorithms.
• 4. Methods for Learning to Quantify.
• 5. Advanced Topics.
• 6. The Quantification Landscape.
• 7. The Road Ahead.

This open access book provides an introduction and an overview of learning to quantify (a.k.a. "quantification"), i.e. the task of training estimators of class proportions in unlabeled data by means of supervised learning. In data science, learning to quantify is a task of its own related to classification yet different from it, since estimating class proportions by simply classifying all data and counting the labels assigned by the classifier is known to often return inaccurate ("biased") class proportion estimates. The book introduces learning to quantify by looking at the supervised learning methods that can be used to perform it, at the evaluation measures and evaluation protocols that should be used for evaluating the quality of the returned predictions, at the numerous fields of human activity in which the use of quantification techniques may provide improved results with respect to the naive use of classification techniques, and at advanced topics in quantification research. The book is suitable to researchers, data scientists, or PhD students, who want to come up to speed with the state of the art in learning to quantify, but also to researchers wishing to apply data science technologies to fields of human activity (e.g., the social sciences, political science, epidemiology, market research) which focus on aggregate ("macro") data rather than on individual ("micro") data.

• Chapte
• r1: A Systematic Review on Supervised & Unsupervised Machine Learning Algorithms for Data Science.- Chapte
• r2: Overview of One-Pass and Discard-After-Learn Concepts for Classification and Clustering in Streaming Environment with Constraints.- Chapte
• r3: Distributed Single-Source Shortest Path Algorithms with Two Dimensional Graph Layout.- Chapte
• r4: Using Non-Negative Tensor Decomposition for Unsupervised Textual Influence Modeling.- Chapte
• r5: Survival Support Vector Machines: A Simulation Study and Its Health-related Application.- Chapte
• r6: Semantic Unsupervised Learning for Word Sense Disambiguation.- Chapte
• r7: Enhanced Tweet Hybrid Recommender System using Unsupervised Topic Modeling and Matrix Factorization based Neural Network.- Chapte
• r8: New Applications of a Supervised Computational Intelligence (CI) Approach: Case Study in Civil Engineering.
• (source: Nielsen Book Data)

This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018). Includes new advances in clustering and classification using semi-supervised and unsupervised learning; Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning; Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning.
(source: Nielsen Book Data)

• Foundations of machine learning
• Using AdaBoost to minimize training error
• Direct bounds on the generalization error
• The margins explanation for boosting's effectiveness
• Game theory, online learning, and boosting
• Loss minimization and generalizations of boosting
• Boosting, convex optimization, and information geometry
• Using confidence-rated weak predictions
• Multiclass classification problems
• Learning to rank
• Attaining the best possible accuracy
• Optimally efficient boosting
• Boosting in continuous time

Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.
(source: Nielsen Book Data)

• Foundations of machine learning
• Using AdaBoost to minimize training error
• Direct bounds on the generalization error
• The margins explanation for boosting's effectiveness
• Game theory, online learning, and boosting
• Loss minimization and generalizations of boosting
• Boosting, convex optimization, and information geometry
• Using confidence-rated weak predictions
• Multiclass classification problems
• Learning to rank
• Attaining the best possible accuracy
• Optimally efficient boosting
• Boosting in continuous time.

An accessible introduction and essential reference for an approach to machine learning that creates highly accurate prediction rules by combining many weak and inaccurate ones. Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.
(source: Nielsen Book Data)

• Foundations of machine learning
• Using AdaBoost to minimize training error
• Direct bounds on the generalization error
• The margins explanation for boosting's effectiveness
• Game theory, online learning, and boosting
• Loss minimization and generalizations of boosting
• Boosting, convex optimization, and information geometry
• Using confidence-rated weak predictions
• Multiclass classification problems
• Learning to rank
• Attaining the best possible accuracy
• Optimally efficient boosting
• Boosting in continuous time.

An accessible introduction and essential reference for an approach to machine learning that creates highly accurate prediction rules by combining many weak and inaccurate ones. Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.
(source: Nielsen Book Data)

• Foundations of machine learning
• Using AdaBoost to minimize training error
• Direct bounds on the generalization error
• The margins explanation for boosting's effectiveness
• Game theory, online learning, and boosting
• Loss minimization and generalizations of boosting
• Boosting, convex optimization, and information geometry
• Using confidence-rated weak predictions
• Multiclass classification problems
• Learning to rank
• Attaining the best possible accuracy
• Optimally efficient boosting
• Boosting in continuous time.

Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.
(source: Nielsen Book Data)

• Foundations of machine learning
• Using AdaBoost to minimize training error
• Direct bounds on the generalization error
• The margins explanation for boosting's effectiveness
• Game theory, online learning, and boosting
• Loss minimization and generalizations of boosting
• Boosting, convex optimization, and information geometry
• Using confidence-rated weak predictions
• Multiclass classification problems
• Learning to rank
• Attaining the best possible accuracy
• Optimally efficient boosting
• Boosting in continuous time.

Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.
(source: Nielsen Book Data)

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

Detailed summary in vernacular field
如今，绝大多数数据科学家和数据工程师基于 高质量的标签数据集训练学习模型。但是，人工 构建训练集既耗时又十分昂贵，以至于很多公司 的机器学习项目无法完成。在本书中，有一种更 为实用的方法，由Wee Hyong Tok、Amit Bahree和Senja Filipi展示如何使用弱监督学习模型创建产品。 你将学习如何通过使用Snorkel（斯坦福大学人工智 能实验室的一个衍生产品），在弱标签数据集上 建立自然语言处理和计算机视觉项目。因为很多 公司研究的机器学习项目从未走出他们的实验室 ，所以本书还提供了如何在真实案例中使用构建 的深度学习模型的指南。 了解弱监督领域的最新进展，包括将其用在数据 科学过程中的方法 使用Snorkel AI进行弱监督和数据编程 获取使用Snorkel标记文本和图像数据集的代码示例 使用弱标签数据集进行文本和图像分类 了解使用 Snorkel 处理大型数据集和使用 Spark 集群扩展标签的注意事项.

• Series Foreword; Preface; 1
• Introduction to Semi-Supervised Learning; 2
• A Taxonomy for Semi-Supervised Learning Methods; 3
• Semi-Supervised Text Classification Using EM; 4
• Risks of Semi-Supervised Learning: How Unlabeled Data Can Degrade Performance of Generative Classifiers; 5
• Probabilistic Semi-Supervised Clustering with Constraints; 6
• Transductive Support Vector Machines; 7
• Semi-Supervised Learning Using Semi- Definite Programming; 8
• Gaussian Processes and the Null-Category Noise Model; 9
• Entropy Regularization; 10
• Data-Dependent Regularization.

• 11
• Label Propagation and Quadratic Criterion12
• The Geometric Basis of Semi-Supervised Learning; 13
• Discrete Regularization; 14
• Semi-Supervised Learning with Conditional Harmonic Mixing; 15
• Graph Kernels by Spectral Transforms; 16- Spectral Methods for Dimensionality Reduction; 17
• Modifying Distances; 18
• Large-Scale Algorithms; 19
• Semi-Supervised Protein Classification Using Cluster Kernels; 20
• Prediction of Protein Function from Networks; 21
• Analysis of Benchmarks; 22
• An Augmented PAC Model for Semi- Supervised Learning.

• 23
• Metric-Based Approaches for Semi- Supervised Regression and Classification24
• Transductive Inference and Semi-Supervised Learning; 25
• A Discussion of Semi-Supervised Learning and Transduction; References; Notation and Symbols; Contributors; Index.

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.Olivier Chapelle and Alexander Zien are Research Scientists and Bernhard Scholkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics in Tubingen. Scholkopf is coauthor of Learning with Kernels (MIT Press, 2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by The MIT Press.
(source: Nielsen Book Data)
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)

• Introduction Graph Construction Learning and Inference Scalability Applications Future Work Bibliography Authors' Biographies Index.
• (source: Nielsen Book Data)

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)

• Introduction
• Overview of supervised learning
• Linear methods for regression
• Linear methods for classification
• Basis expansions and regularization
• Kernel smoothing methods
• Model assessment and selection
• Model inference and averaging
• Additive models, trees, and related methods
• Boosting and additive trees
• Neural networks
• Support vector machines and flexible discriminants
• Prototype methods and nearest-neighbors
• Unsupervised learning
• Random forests
• Ensemble learning
• Undirected graphical models
• 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)