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Science/Engineering Statistical Mechanics, Third Edition
Science/Engineering Essentials of Toxic Chemical Risk: Science and Society
Science/Engineering Telefoncoaching: So machen Sie aus Ihren Mitarbeitern Telefonprofis
Science/Engineering Wireless Communications (Wiley - IEEE)
Science/Engineering Posttraumatische Belastungsstörungen (German Edition)
Science/Engineering Lernplattformen in Schulen: Ansätze für E-Learning und Blended Learning in Präsenzklassen (1 Auflage)
Science/Engineering Stochastik für Einsteiger: Eine Einführung in die faszinierende Welt des Zufalls. Mit über 220 Übungsaufgaben und Lösungen {Repost}
Science/Engineering Testtheorie und Fragebogenkonstruktion (Springer-Lehrbuch)
Science/Engineering Centrifugal Pumps, 2nd Edition
Science/Engineering Computational Intelligence for Modelling and Prediction (Studies in Computational Intelligence) 1 edition {Repost}
Science/Engineering Networks, Crowds, and Markets: Reasoning About a Highly Connected World {repost}
Science/Engineering Introduction to Biophotonics (repost)
Science/Engineering The Art and Science of Psychotherapy (repost)
Science/Engineering Advances in Chemical Physics - Volume 15: Stochastic Processes in Chemical Physics
Science/Engineering "Emulsion Science: Basic Principles" (repost)
Science/Engineering Elementary Principles of Chemical Processes 3rd edition
Science/Engineering Boundary Element Analysis (repost)
Science/Engineering Collection of books on physics 2
Science/Engineering A Practical Handbook of Preparative HPLC by Donald A. Wellings (Repost)
Science/Engineering Reviews of Environmental Contamination and Toxicology 184 by George W. Ware
Science/Engineering Essentials of Toxic Chemical Risk: Science and Society
Science/Engineering Telefoncoaching: So machen Sie aus Ihren Mitarbeitern Telefonprofis
Science/Engineering Wireless Communications (Wiley - IEEE)
Science/Engineering Posttraumatische Belastungsstörungen (German Edition)
Science/Engineering Lernplattformen in Schulen: Ansätze für E-Learning und Blended Learning in Präsenzklassen (1 Auflage)
Science/Engineering Stochastik für Einsteiger: Eine Einführung in die faszinierende Welt des Zufalls. Mit über 220 Übungsaufgaben und Lösungen {Repost}
Science/Engineering Testtheorie und Fragebogenkonstruktion (Springer-Lehrbuch)
Science/Engineering Centrifugal Pumps, 2nd Edition
Science/Engineering Computational Intelligence for Modelling and Prediction (Studies in Computational Intelligence) 1 edition {Repost}
Science/Engineering Networks, Crowds, and Markets: Reasoning About a Highly Connected World {repost}
Science/Engineering Introduction to Biophotonics (repost)
Science/Engineering The Art and Science of Psychotherapy (repost)
Science/Engineering Advances in Chemical Physics - Volume 15: Stochastic Processes in Chemical Physics
Science/Engineering "Emulsion Science: Basic Principles" (repost)
Science/Engineering Elementary Principles of Chemical Processes 3rd edition
Science/Engineering Boundary Element Analysis (repost)
Science/Engineering Collection of books on physics 2
Science/Engineering A Practical Handbook of Preparative HPLC by Donald A. Wellings (Repost)
Science/Engineering Reviews of Environmental Contamination and Toxicology 184 by George W. Ware
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Science/Engineering Introduction to Semi-Supervised Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Posted on 2010-04-14
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More Xiaojin Zhu, Andrew B. Goldberg, Ronald Brachman, Thomas Dietterich, "Introduction to Semi-Supervised Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)" 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 is unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data is 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 is 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. Links or
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