Active Learning

Nonfiction, Computers, Advanced Computing, Theory, Artificial Intelligence, General Computing
Cover of the book Active Learning by Burr Settles, Morgan & Claypool Publishers
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Burr Settles ISBN: 9781681731766
Publisher: Morgan & Claypool Publishers Publication: July 1, 2012
Imprint: Morgan & Claypool Publishers Language: English
Author: Burr Settles
ISBN: 9781681731766
Publisher: Morgan & Claypool Publishers
Publication: July 1, 2012
Imprint: Morgan & Claypool Publishers
Language: English

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

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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

More books from Morgan & Claypool Publishers

Cover of the book Customizable Computing by Burr Settles
Cover of the book Game Theory for Data Science by Burr Settles
Cover of the book An Introduction to the Physics of Nuclear Medicine by Burr Settles
Cover of the book An Introduction to Chemical Kinetics by Burr Settles
Cover of the book A Practical Introduction to Beam Physics and Particle Accelerators by Burr Settles
Cover of the book Relativistic Many-Body Theory and Statistical Mechanics by Burr Settles
Cover of the book Incidental Exposure to Online News by Burr Settles
Cover of the book Researching Serendipity in Digital Information Environments by Burr Settles
Cover of the book Provenance by Burr Settles
Cover of the book The Ringed Planet by Burr Settles
Cover of the book Advances in Nanomaterials for Drug Delivery by Burr Settles
Cover of the book Defining and Measuring Nature by Burr Settles
Cover of the book Thermal Properties of Matter by Burr Settles
Cover of the book Ensemble Methods in Data Mining by Burr Settles
Cover of the book Twin-Win Research by Burr Settles
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy