Stock Image

Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation

Masashi Sugiyama

3 ratings by Goodreads
ISBN 10: 0262017091 / ISBN 13: 9780262017091
Published by MIT Press Ltd
New Condition: New Hardcover
From THE SAINT BOOKSTORE (Southport, United Kingdom)

AbeBooks Seller Since 14 June 2006 Seller Rating 5-star rating

Quantity Available: 2

Buy New
Price: 38.66 Convert Currency
Shipping: 6.94 From United Kingdom to U.S.A. Destination, rates & speeds
Add to basket

About this Item

New copy - Usually dispatched within 2 working days. Bookseller Inventory # B9780262017091

Ask Seller a Question

Bibliographic Details

Title: Machine Learning in Non-Stationary ...

Publisher: MIT Press Ltd

Binding: Hardback

Book Condition: New

About this title

Synopsis:

Theory, algorithms, and applications of machine learning techniques to overcome "covariate shift" non-stationarity. As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of-the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non-stationarity.

Product Description:

As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of-the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non-stationarity.

"About this title" may belong to another edition of this title.

Store Description

The Saint Bookstore has a range of over 1 million titles available.

Visit Seller's Storefront

Terms of Sale:

Please order through the Abebooks checkout. We only take orders through Abebooks - We don't take direct orders by email or phone.

Refunds or Returns: A full refund of the purchase price will be given if returned within 30 days in undamaged condition.

As a seller on abebooks we adhere to the terms explained at http://www.abebooks.co.uk/docs/HelpCentral/buyerIndex.shtml - if you require further assistance please email us at orders@thesaintbookstore.co.uk

Shipping Terms:

Most orders usually ship within 1-3 business days, but some can take up to 7 days.

List this Seller's Books

Payment Methods
accepted by seller

Visa Mastercard American Express