Settles burr (12 Ergebnisse)
Sprache: Englisch
Verlag: Springer International Publishing AG, CH, 2012
Serie: Buch 2 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Paperback. Zustand: New. 1st. The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose "queries," usually in the form of unlabeled data instances to be labeled by an "oracle" (e.g., a human a…nnotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain. This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or "query selection frameworks." We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities. Table of Contents: Automating Inquiry / Uncertainty Sampling / Searching Through the Hypothesis Space / Minimizing Expected Error and Variance / Exploiting Structure in Data / Theory / Practical Considerations.
Sprache: Englisch
Verlag: Springer, 2012
Serie: Buch 2 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Zustand: New. In English.
Sprache: Englisch
Verlag: Springer, 2012
Serie: Buch 2 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Sprache: Englisch
Verlag: Springer, 2012
Serie: Buch 2 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose 'queries,' usually in the form of unlabeled data insta…nces to be labeled by an 'oracle' (e.g., a human annotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain. This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or 'query selection frameworks.' We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities. Table of Contents: Automating Inquiry / Uncertainty Sampling / Searching Through the Hypothesis Space / Minimizing Expected Error and Variance / Exploiting Structure in Data / Theory / Practical Considerations.
Sprache: Englisch
Verlag: Springer, 2012
Serie: Buch 2 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Taschenbuch. Zustand: Neu. Active Learning | Burr Settles | Taschenbuch | Synthesis Lectures on Artificial Intelligence and Machine Learning | xiv | Englisch | 2012 | Springer | EAN 9783031004322 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]c…om | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer International Publishing AG, CH, 2012
Serie: Buch 2 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
- Erstausgabe
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Paperback. Zustand: New. 1st. The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose "queries," usually in the form of unlabeled data instances to be labeled by an "oracle" (e.g., a human a…nnotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain. This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or "query selection frameworks." We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities. Table of Contents: Automating Inquiry / Uncertainty Sampling / Searching Through the Hypothesis Space / Minimizing Expected Error and Variance / Exploiting Structure in Data / Theory / Practical Considerations.
Sprache: Englisch
Verlag: Springer, 2012
Serie: Buch 2 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Zustand: new. Questo è un articolo print on demand.
Sprache: Englisch
Verlag: Springer, 2012
Serie: Buch 2 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Sprache: Englisch
Verlag: Springer International Publishing Aug 2012, 2012
Serie: Buch 2 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose 'queries,' usually in the form of unla…beled data instances to be labeled by an 'oracle' (e.g., a human annotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain. This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or 'query selection frameworks.' We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities. Table of Contents: Automating Inquiry / Uncertainty Sampling / Searching Through the Hypothesis Space / Minimizing Expected Error and Variance / Exploiting Structure in Data / Theory / Practical Considerations 116 pp. Englisch.
Sprache: Englisch
Verlag: Springer, 2012
Serie: Buch 2 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
- Print-on-Demand
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Sprache: Englisch
Verlag: Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2012
Serie: Buch 2 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose queries, usually in the… form of unlabeled data instances to.
Sprache: Englisch
Verlag: Springer, Springer Aug 2012, 2012
Serie: Buch 2 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
- Print-on-Demand
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose 'queries,' usually in the form of unlabele…d data instances to be labeled by an 'oracle' (e.g., a human annotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain. This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or 'query selection frameworks.' We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities. Table of Contents: Automating Inquiry / Uncertainty Sampling / Searching Through the Hypothesis Space / Minimizing Expected Error and Variance / Exploiting Structure in Data / Theory / Practical ConsiderationsSpringer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 116 pp. Englisch.
