Zhu xiaojin (26 risultati)

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000. Zhu, Xiaojin (illustratore).

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000. Zhu, Xiaojin (illustratore).

Paperback. Condizione: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Paperback. Condizione: New. 1st. 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. …

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New. 1st edition NO-PA16APR2015-KAP.

Paperback. Condizione: Brand New. 9.25x7.51 inches. In Stock.

Condizione: New. In English.

Paperback. Condizione: Used; Good. ***Simply Brit*** Welcome to our online used book store, where affordability meets great quality. Dive into a world of captivating reads without breaking the bank. We take pride in offering a wide selection of used books, from classics to hidden gems, ensuring there is something for every literary palate. All orders are shipped within 24 hours and our lightning fast-delivery within 48 hours coupled with our prompt customer service ensures a smooth journey from ordering to delivery. Discover the joy of reading with us, your trusted source for affordable books that do not compromise on quality.…

Condizione: NEW.

Condizione: New. 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 paradi.

Condizione: New. New.

Taschenbuch. Condizione: Neu. Introduction to Semi-Supervised Learning | Xiaojin Zhu (u. a.) | Taschenbuch | Synthesis Lectures on Artificial Intelligence and Machine Learning | xii | Englisch | 2009 | Springer | EAN 9783031004209 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Paperback. Condizione: New. 1st. 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. …

Condizione: New. Print on Demand. Zhu, Xiaojin (illustratore).

Condizione: New. Print on Demand. Zhu, Xiaojin (illustratore).

Condizione: New. PRINT ON DEMAND. Zhu, Xiaojin (illustratore).

Condizione: new. Questo è un articolo print on demand.

Condizione: New. Print on Demand.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Sometimes it seems only a nurse can bring technical information down to an understanding that an ordinary person can grasp. The Nurse Florence® book series provides high quality medical information that even a child can grasp. By introducing young kids to correct terminology and science concepts at an early age, we can help increase our children's health literacy level as well as help to prepare them for courses and jobs in Science, Technology, Engineering, and Math. We need more scientists so I hope that many children will enjoy this book series and consider a job involving science.Introducing Some Medical Words to Kids in Every Book®A Movement of Global Health Promotion and LiteracyDow Creative Enterprises®Help Civilization Reach Its Potential®. Zhu, Xiaojin (illustratore).…

Condizione: New. PRINT ON DEMAND.

Paperback. Condizione: new. Paperback. Sometimes it seems only a nurse can bring technical information down to an understanding that an ordinary person can grasp. The Nurse Florence(R) book series provides high quality medical information that even a child can grasp. By introducing young kids to correct terminology and science concepts at an early age, we can help increase our children's health literacy level as well as help to prepare them for courses and jobs in Science, Technology, Engineering, and Math. We need more scientists so I hope that many children will enjoy this book series and consider a job involving science. Introducing Some Medical Words to Kids in Every Book(R) A Movement of Global Health Promotion and Literacy Dow Creative Enterprises(R) Help Civilization Reach Its Potential(R) This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Zhu, Xiaojin (illustratore).…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 OutlookSpringer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 132 pp. Englisch. …

Taschenbuch. Condizione: Neu. Nurse Florence®, What is Brugada Syndrome? | Michael Dow | Taschenbuch | Englisch | 2025 | [.] | EAN 9781300234876 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Zhu, Xiaojin (illustratore).…