Semi Supervised Learning
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Introduction To Semi Supervised Learning
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Author : Xiaojin Zhu
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Introduction To Semi Supervised Learning written by Xiaojin Zhu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
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. Table of Contents: 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
Advanced Supervised And Semi Supervised Learning
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Author : Massih-Reza Amini
language : en
Publisher: Springer Nature
Release Date : 2025-11-17
Advanced Supervised And Semi Supervised Learning written by Massih-Reza Amini and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-17 with Computers categories.
Machine learning is one of the leading areas of artificial intelligence. It concerns the study and development of quantitative models that enable a computer to carry out operations without having been expressly programmed to do so. In this situation, learning is about identifying complex shapes and making intelligent decisions. The challenge in completing this task, given all the available inputs, is that the set of potential decisions is typically quite difficult to enumerate. Machine learning algorithms have been developed with the goal of learning about the problem to be handled based on a collection of limited data from this problem in order to get around this challenge. This textbook presents the scientific foundations of supervised learning theory, the most widespread algorithms developed according to this framework, as well as the semi-supervised and the learning-to-rank frameworks, at a level accessible to master's students. The aim of the book is to provide a coherent presentation linking the theory to the algorithms developed in this field. In addition, this study is not limited to the presentation of these foundations, but it also presents exercises, and is intended for readers who seek to understand the functioning of these models sometimes designated as black boxes.
Graph Based Semi Supervised Learning
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Author : Amarnag Subramanya
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2014-07-01
Graph Based Semi Supervised Learning written by Amarnag Subramanya and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-01 with Computers categories.
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. Table of Contents: Introduction / Graph Construction / Learning and Inference / Scalability / Applications / Future Work / Bibliography / Authors' Biographies / Index
Introduction To Semi Supervised Learning
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Author : Zenglin Xu
language : en
Publisher: CRC PressI Llc
Release Date : 2015-05-15
Introduction To Semi Supervised Learning written by Zenglin Xu and has been published by CRC PressI Llc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-15 with Business & Economics categories.
Including the historical background and recent advances in the field as well as theoretical perspectives and real-world applications, this book outlines a systematic framework for implementing semi-supervised learning methods. It provides a toolbox on semi-supervised learning algorithms, presenting illustrations and examples of each algorithm. The book defines and distinguishes supervised learning, unsupervised learning, semi-supervised learning, and other relevant learning tasks. It discusses important semi-supervised learning models, including generative models for semi-supervised learning, semi-supervised support vector machines, and graph-based semi-supervised learning methods.
Semi Supervised Learning
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Author : Guoqiang Zhong
language : en
Publisher:
Release Date : 2018-06
Semi Supervised Learning written by Guoqiang Zhong and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06 with COMPUTERS categories.
Semi-supervised learning is an important area of machine learning. It deals with problems that involve a lot of unlabeled data and very scarce labeled data. The book focuses on some state-of-the-art research on semi-supervised learning. In the first chapter, Weng, Dornaika and Jin introduce a graph construction algorithm named the constrained data self-representative graph construction (CSRGC). In the second chapter, to reduce the graph construction complexity, Zhang et al. use anchors that were a special subset chosen from the original data to construct the full graph, while randomness was injected into graphs to improve the classification accuracy and deal with the high dimensionality issue. In the third chapter, Dornaika et al. introduces a kernel version of the Flexible Manifold Embedding (KFME) algorithm. In the fourth chapter, Zhang et al. present an efficient and robust graph-based transductive classification method known as the minimum tree cut (MTC), for large scale applications. In the fifth chapter, Salazar, Safont and Vergara investigated the performance of semi-supervised learning methods in two-class classification problems with a scarce population of one of the classes. In the sixth chapter, by breaking the sample identically and independently distributed (i.i.d.) assumption, one novel framework called the field support vector machine (F-SVM) with both classification (F-SVC) and regression (F-SVR) purposes is introduced. In the seventh chapter, Gong employs the curriculum learning methodology by investigating the difficulty of classifying every unlabeled example. As a result, an optimized classification sequence was generated during the iterative propagations, and the unlabeled examples are logically classified from simple to difficult. In the eighth chapter, Tang combines semi-supervised learning with geo-tagged photo streams and concept detection to explore situation recognition. This book is suitable for university students (undergraduate or graduate) in computer science, statistics, electrical engineering, or anyone else who would potentially use machine learning algorithms; professors, who research artificial intelligence, pattern recognition, machine learning, data mining and related fields; and engineers, who apply machine learning models into their products.
Semi Supervised Learning With Partially Labeled Examples
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Author : Nam Hoang Nguyen
language : en
Publisher:
Release Date : 2010
Semi Supervised Learning With Partially Labeled Examples written by Nam Hoang Nguyen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
Traditionally, machine learning community has been focused on supervised learning where the source of learning is fully labeled examples including both input features and corresponding output labels. As one way to alleviate the costly effort of collecting fully labeled examples, semi-supervised learning usually concentrates on utilizing a large amount of unlabeled examples together with a relatively small number of fully labeled examples to build better classifiers. Even though many semi-supervised learning algorithms are able to take advantage of unlabeled examples, there is a significant amount of effort in designing good models, features, kernels, and similarity functions. In this dissertation, we focus on semi-supervised learning with partially labeled examples. Partially labeled data can be viewed as a trade-off between fully labeled data and unlabeled data, which can provide additional discriminative information in comparison to unlabeled data and requires less human effort to collect than fully labeled data. In our setting of semi-supervised learning with partially labeled examples, the learning method is provided with a large amount of partially labeled examples and is usually augmented with a relatively small set of fully labeled examples. Our main goal is to integrate partially labeled examples into the conventional learning framework, i.e. to build a more accurate classifier. The dissertation addresses four different semi-supervised learning problems in presence of partially labeled examples. In addition, we summarize general principles for the semi-supervised learning with partially labeled examples.
Semi Supervised Learning
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Author : Olivier Chapelle
language : en
Publisher: MIT Press
Release Date : 2010-01-22
Semi Supervised Learning written by Olivier Chapelle and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-01-22 with Computers categories.
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.
Semisupervised Learning For Computational Linguistics
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Author : Steven Abney
language : en
Publisher: CRC Press
Release Date : 2007-09-17
Semisupervised Learning For Computational Linguistics written by Steven Abney and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-09-17 with Business & Economics categories.
The rapid advancement in the theoretical understanding of statistical and machine learning methods for semisupervised learning has made it difficult for nonspecialists to keep up to date in the field. Providing a broad, accessible treatment of the theory as well as linguistic applications, Semisupervised Learning for Computational Linguistics offer
Fundamental Limitations Of Semi Supervised Learning
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Author : Tyler Tian Lu
language : en
Publisher:
Release Date : 2009
Fundamental Limitations Of Semi Supervised Learning written by Tyler Tian Lu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
The emergence of a new paradigm in machine learning known as semi-supervised learning (SSL) has seen benefits to many applications where labeled data is expensive to obtain. However, unlike supervised learning (SL), which enjoys a rich and deep theoretical foundation, semi-supervised learning, which uses additional unlabeled data for training, still remains a theoretical mystery lacking a sound fundamental understanding. The purpose of this research thesis is to take a first step towards bridging this theory-practice gap. We focus on investigating the inherent limitations of the benefits SSL can provide over SL. We develop a framework under which one can analyze the potential benefits, as measured by the sample complexity of SSL. Our framework is utopian in the sense that a SSL algorithm trains on a labeled sample and an unlabeled distribution, as opposed to an unlabeled sample in the usual SSL model. Thus, any lower bound on the sample complexity of SSL in this model implies lower bounds in the usual model. Roughly, our conclusion is that unless the learner is absolutely certain there is some non-trivial relationship between labels and the unlabeled distribution ("SSL type assumption"), SSL cannot provide significant advantages over SL. Technically speaking, we show that the sample complexity of SSL is no more than a constant factor better than SL for any unlabeled distribution, under a no-prior-knowledge setting (i.e. without SSL type assumptions). We prove that for the class of thresholds in the realizable setting the sample complexity of SL is at most twice that of SSL. Also, we prove that in the agnostic setting for the classes of thresholds and union of intervals the sample complexity of SL is at most a constant factor larger than that of SSL. We conjecture this to be a general phenomenon applying to any hypothesis class. We also discuss issues regarding SSL type assumptions, and in particular the popular cluster assumption. We give examples that show even in the most accommodating circumstances, learning under the cluster assumption can be hazardous and lead to prediction performance much worse than simply ignoring the unlabeled data and doing supervised learning. We conclude with a look into future research directions that build on our investigation.
Semi Supervised Learning And Domain Adaptation In Natural Language Processing
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Author : Anders Søgaard
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Semi Supervised Learning And Domain Adaptation In Natural Language Processing written by Anders Søgaard and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
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.