Logo - springer
Slogan - springer

Computer Science - Artificial Intelligence | Preference Learning

Preference Learning

Fürnkranz, Johannes, Hüllermeier, Eyke (Eds.)

2011, IX, 466 p.

Available Formats:
eBook
Information

Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.

You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.

After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.

 
$119.00

(net) price for USA

ISBN 978-3-642-14125-6

digitally watermarked, no DRM

Included Format: PDF and EPUB

download immediately after purchase


learn more about Springer eBooks

add to marked items

Hardcover
Information

Hardcover version

You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.

Standard shipping is free of charge for individual customers.

 
$159.00

(net) price for USA

ISBN 978-3-642-14124-9

free shipping for individuals worldwide

usually dispatched within 3 to 5 business days


add to marked items

Softcover
Information

Softcover (also known as softback) version.

You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.

Standard shipping is free of charge for individual customers.

 
$159.00

(net) price for USA

ISBN 978-3-642-42230-0

free shipping for individuals worldwide

usually dispatched within 3 to 5 business days


add to marked items

  • This is the first book dedicated to this topic
  • This topic has attracted considerable attention in artificial intelligence research in recent years
  • A comprehensive treatment
The topic of preferences is a new branch of machine learning and data mining, and it has attracted considerable attention in artificial intelligence research in recent years. Representing and processing knowledge in terms of preferences is appealing as it allows one to specify desires in a declarative way, to combine qualitative and quantitative modes of reasoning, and to deal with inconsistencies and exceptions in a flexible manner. Preference learning is concerned with the acquisition of preference models from data – it involves learning from observations that reveal information about the preferences of an individual or a class of individuals, and building models that generalize beyond such training data. This is the first book dedicated to this topic, and the treatment is comprehensive. The editors first offer a thorough introduction, including a systematic categorization according to learning task and learning technique, along with a unified notation. The remainder of the book is organized into parts that follow the developed framework, complementing survey articles with in-depth treatises of current research topics in this area. The book will be of interest to researchers and practitioners in artificial intelligence, in particular machine learning and data mining, and in fields such as multicriteria decision-making and operations research.

Content Level » Research

Keywords » Artificial intelligence - Data mining - Information retrieval - Instance ranking - Label ranking - Learning - Machine learning - Multicriteria decision-making - Object ranking - Operations research - Preference learning - Preference prediction - Reasoning - Recommender systems - Supevised learning

Related subjects » Artificial Intelligence - Database Management & Information Retrieval

Table of contents / Preface / Sample pages 

Popular Content within this publication 

 

Articles

Read this Book on Springerlink

Services for this book

New Book Alert

Get alerted on new Springer publications in the subject area of Artificial Intelligence (incl. Robotics).