Active Learning

Burr Settles

ISBN 10: 3031004329 / ISBN 13: 9783031004322

Verlag: Springer Nature Switzerland, Springer International Publishing Aug 2012, 2012

Beschreibung

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 unlabeled 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 GmbH, Tiergartenstr. 17, 69121 Heidelberg 116 pp. Englisch.

Bestandsnummer des Verkäufers 9783031004322

Bibliografische Details

• Titel: Active Learning
• Verlag: Springer Nature Switzerland, Springer International Publishing Aug 2012
• Erscheinungsjahr: 2012
• Sprache: Englisch
• ISBN-10: 3031004329
• ISBN-13: 9783031004322
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 235B x 191 H x 7 T Millimeter Breite: 235 mm, Höhe: 191 mm, Tiefe: 7 mm
• Gewicht: 233 Gramm

Über diesen Titel

Inhaltsangabe

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 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

Über die Autorin bzw. den Autor

Burr Settles leads the research group at Duolingo, an award-winning website and mobile app offering free language education for the world. He also runs FAWM.ORG, a global annual songwriting experiment. His research has been published in NIPS, ICML, AAAI, ACL, EMNLP, NAACL-HLT, and CHI, and has been covered by The New York Times, Slate, Forbes, WIRED, and the BBC among others. In past lives, he was a postdoc at Carnegie Mellon and earned a PhD from UW-Madison.