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Learning with Partially Labeled and Interdependent Data

Author : Massih-Reza Amini

Publisher : Springer

Page : 113 pages

File Size : 42,81 MB

Release : 2015-05-07

Category : Computers

ISBN : 3319157264

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Book Description

This book develops two key machine learning principles: the semi-supervised paradigm and learning with interdependent data. It reveals new applications, primarily web related, that transgress the classical machine learning framework through learning with interdependent data. The book traces how the semi-supervised paradigm and the learning to rank paradigm emerged from new web applications, leading to a massive production of heterogeneous textual data. It explains how semi-supervised learning techniques are widely used, but only allow a limited analysis of the information content and thus do not meet the demands of many web-related tasks. Later chapters deal with the development of learning methods for ranking entities in a large collection with respect to precise information needed. In some cases, learning a ranking function can be reduced to learning a classification function over the pairs of examples. The book proves that this task can be efficiently tackled in a new framework: learning with interdependent data. Researchers and professionals in machine learning will find these new perspectives and solutions valuable. Learning with Partially Labeled and Interdependent Data is also useful for advanced-level students of computer science, particularly those focused on statistics and learning.



Artificial Neural Networks in Pattern Recognition

Author : Friedhelm Schwenker

Publisher : Springer

Page : 342 pages

File Size : 43,72 MB

Release : 2016-09-08

Category : Computers

ISBN : 3319461826

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Book Description

This book constitutes the refereed proceedings of the 7th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2016, held in Ulm, Germany, in September 2016. The 25 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 32 submissions for inclusion in this volume. The workshop will act as a major forum for international researchers and practitioners working in all areas of neural network- and machine learning-based pattern recognition to present and discuss the latest research, results, and ideas in these areas.



Advances in Information Retrieval

Author : Leif Azzopardi

Publisher : Springer

Page : 890 pages

File Size : 36,95 MB

Release : 2019-04-06

Category : Computers

ISBN : 3030157121

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Book Description

This two-volume set LNCS 11437 and 11438 constitutes the refereed proceedings of the 41st European Conference on IR Research, ECIR 2019, held in Cologne, Germany, in April 2019. The 48 full papers presented together with 2 keynote papers, 44 short papers, 8 demonstration papers, 8 invited CLEF papers, 11 doctoral consortium papers, 4 workshop papers, and 4 tutorials were carefully reviewed and selected from 365 submissions. They were organized in topical sections named: Modeling Relations; Classification and Search; Recommender Systems; Graphs; Query Analytics; Representation; Reproducibility (Systems); Reproducibility (Application); Neural IR; Cross Lingual IR; QA and Conversational Search; Topic Modeling; Metrics; Image IR; Short Papers; Demonstration Papers; CLEF Organizers Lab Track; Doctoral Consortium Papers; Workshops; and Tutorials.



Security and Resilience of Control Systems

Author : Hideaki Ishii

Publisher : Springer Nature

Page : 229 pages

File Size : 49,29 MB

Release : 2022-01-22

Category : Technology & Engineering

ISBN : 3030832368

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Book Description

This book comprises a set of chapters that introduce various topics pertinent to novel approaches towards enhancing cyber-physical measures for increased security and resilience levels in control systems. The unifying theme of these approaches lies in the utilization of knowledge and models of the physical systems, rather than an attempt to reinvigorate conventional IT-based security measures. The contributing authors present perspectives on network security, game theory, and control, as well as views on how these disciplines can be combined to design resilient, safe, and secure control systems. The book explores how attacks in different forms, such as false data injections and denial-of-service can be very harmful, and may not be detected unless the security measures exploit the physical models. Several applications are discussed, power systems being considered most thoroughly. Because of its interdisciplinary nature—techniques from systems control, game theory, signal processing and computer science all make contributions—Security and Resilience of Control Systems will be of interest to academics, practitioners and graduate students with a broad spectrum of interests.



Introduction to Semi-supervised Learning

Author : Xiaojin Zhu

Publisher : Morgan & Claypool Publishers

Page : 131 pages

File Size : 36,15 MB

Release : 2009

Category : Computers

ISBN : 1598295470

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Book Description

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



Data Classification

Author : Charu C. Aggarwal

Publisher : CRC Press

Page : 710 pages

File Size : 35,88 MB

Release : 2014-07-25

Category : Business & Economics

ISBN : 1498760589

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Book Description

Comprehensive Coverage of the Entire Area of ClassificationResearch on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlyi



Optimization, Learning, and Control for Interdependent Complex Networks

Author : M. Hadi Amini

Publisher : Springer Nature

Page : 306 pages

File Size : 48,33 MB

Release : 2020-02-22

Category : Technology & Engineering

ISBN : 3030340945

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Book Description

This book focuses on a wide range of optimization, learning, and control algorithms for interdependent complex networks and their role in smart cities operation, smart energy systems, and intelligent transportation networks. It paves the way for researchers working on optimization, learning, and control spread over the fields of computer science, operation research, electrical engineering, civil engineering, and system engineering. This book also covers optimization algorithms for large-scale problems from theoretical foundations to real-world applications, learning-based methods to enable intelligence in smart cities, and control techniques to deal with the optimal and robust operation of complex systems. It further introduces novel algorithms for data analytics in large-scale interdependent complex networks. • Specifies the importance of efficient theoretical optimization and learning methods in dealing with emerging problems in the context of interdependent networks • Provides a comprehensive investigation of advance data analytics and machine learning algorithms for large-scale complex networks • Presents basics and mathematical foundations needed to enable efficient decision making and intelligence in interdependent complex networks M. Hadi Amini is an Assistant Professor at the School of Computing and Information Sciences at Florida International University (FIU). He is also the founding director of Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab). He received his Ph.D. and M.Sc. from Carnegie Mellon University in 2019 and 2015 respectively. He also holds a doctoral degree in Computer Science and Technology. Prior to that, he received M.Sc. from Tarbiat Modares University in 2013, and the B.Sc. from Sharif University of Technology in 2011.



Introduction to Semi-Supervised Learning

Author : Xiaojin Geffner

Publisher : Springer Nature

Page : 116 pages

File Size : 25,32 MB

Release : 2022-05-31

Category : Computers

ISBN : 3031015487

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Book Description

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



Systems Engineering and Artificial Intelligence

Author : William F. Lawless

Publisher : Springer Nature

Page : 566 pages

File Size : 28,79 MB

Release : 2021-11-02

Category : Computers

ISBN : 3030772837

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Book Description

This book provides a broad overview of the benefits from a Systems Engineering design philosophy in architecting complex systems composed of artificial intelligence (AI), machine learning (ML) and humans situated in chaotic environments. The major topics include emergence, verification and validation of systems using AI/ML and human systems integration to develop robust and effective human-machine teams—where the machines may have varying degrees of autonomy due to the sophistication of their embedded AI/ML. The chapters not only describe what has been learned, but also raise questions that must be answered to further advance the general Science of Autonomy. The science of how humans and machines operate as a team requires insights from, among others, disciplines such as the social sciences, national and international jurisprudence, ethics and policy, and sociology and psychology. The social sciences inform how context is constructed, how trust is affected when humans and machines depend upon each other and how human-machine teams need a shared language of explanation. National and international jurisprudence determine legal responsibilities of non-trivial human-machine failures, ethical standards shape global policy, and sociology provides a basis for understanding team norms across cultures. Insights from psychology may help us to understand the negative impact on humans if AI/ML based machines begin to outperform their human teammates and consequently diminish their value or importance. This book invites professionals and the curious alike to witness a new frontier open as the Science of Autonomy emerges.



Preference Learning

Author : Johannes Fürnkranz

Publisher : Springer Science & Business Media

Page : 457 pages

File Size : 24,33 MB

Release : 2010-11-19

Category : Computers

ISBN : 3642141250

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Book Description

The topic of preferences is a new branch of machine learning and data mining, and it has attracted considerable attention in artificial intelligence research in previous years. It involves learning from observations that reveal information about the preferences of an individual or a class of individuals. Representing and processing knowledge in terms of preferences is appealing as it allows one to specify desires in a declarative way, to combine qualitative and quantitative modes of reasoning, and to deal with inconsistencies and exceptions in a flexible manner. And, generalizing beyond training data, models thus learned may be used for preference prediction. This is the first book dedicated to this topic, and the treatment is comprehensive. The editors first offer a thorough introduction, including a systematic categorization according to learning task and learning technique, along with a unified notation. The first half of the book is organized into parts on label ranking, instance ranking, and object ranking; while the second half is organized into parts on applications of preference learning in multiattribute domains, information retrieval, and recommender systems. The book will be of interest to researchers and practitioners in artificial intelligence, in particular machine learning and data mining, and in fields such as multicriteria decision-making and operations research.