Masashi Sugiyama (154 results)

Hardcover. Condition: Near Fine. Dust Jacket Condition: Near Fine. 1st Edition. Date on copy right is 2009 and has "1" on the number line. Interior pages are clean and bright; binding is firm - no loose pages. There are no tears, writing or folds on inside pages. . This NOT an Ex-lib; A remainder- mark on bottom edge. DJ bright and clean with light shelf wear. Not price clipped . …

Softcover. Condition: As New. Leichte Kratzer / Abnutzungen / Druckstellen. This volume provides an overview of recent efforts in the machine learning community to address dataset and covariate shift, which occur when training and test inputs and outputs have differing distributions. Dataset shift is a prevalent issue in predictive modeling, arising when the joint distribution of inputs and outputs varies between training and testing phases. Covariate shift, a specific instance of dataset shift, happens when only the input distribution changes. This problem is common in practical applications, influenced by factors such as experimental design bias and the unreliability of testing conditions during training. For example, email spam filters may struggle to identify new spam forms that differ from the training data. Despite its significance, dataset shift has historically received limited attention compared to related topics like semi-supervised and active learning. The chapters in this volume provide a mathematical and philosophical introduction to the issue, contextualize dataset shift within transfer learning, transduction, local learning, active learning, and semi-supervised learning, and present theoretical perspectives, including decision-theoretic and Bayesian views. Additionally, algorithms for addressing covariate shift are discussed.…

Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Hardcover. Condition: As New. Dust Jacket Condition: As New.

Condition: New.

Paperback or Softback. Condition: New. Dataset Shift in Machine Learning. Book.

hardcover. Condition: Very Good. Very Good Condition - May show some limited signs of wear and may have a remainder mark. Pages and dust cover are intact and not marred by notes or highlighting.

hardcover. Condition: Good.

hardcover. Condition: Fine. LIKE NEW!!! Has a red or black remainder mark on bottom/exterior edge of pages.

Condition: New.

Condition: As New. Unread book in perfect condition.

Condition: acceptable. Used - Acceptable: All pages and the cover are intact, but shrink wrap, dust covers, or boxed set case may be missing. Pages may include limited notes, highlighting, or minor water damage but the text is readable. Pages may include limited notes and highlighting, but the text cannot be obscured or unreadable. Any access codes or passwords originally included with the book may be expired, used or no longer valid. Image is stock photo and cover art edition may be different than pictured.…

hardcover. Condition: Fine.

hardcover. Condition: Very Good. very good condition, pages are clean and free of markings, minimal wear to cover, has dust jacket where applicable, ships same or next business day.

Paperback. Condition: New.

Hardcover. Condition: New. New large hardback w/ jacket.

Condition: As New. Unread book in perfect condition.

Condition: New. pp. 320.

Condition: New.

Condition: New. In English.

Trade paperback. Condition: New in new dust jacket. First edition. INTERNATIONAL EDITION. ***INTERNATIONAL EDITION*** Read carefully before purchase: This book is the international edition in mint condition with the different ISBN and book cover design, the major content is printed in full English as same as the original North American edition. The book printed in black and white, generally send in twenty-four hours after the order confirmed. All shipments contain tracking numbers. Great professional textbook selling experience and expedite shipping service. Trade paperback (US). Glued binding. Contains: Illustrations, black & white. Audience: General/trade.…

Soft cover. Condition: New. Dust Jacket Condition: New. International Edition. **INTERNATIONAL EDITION** Read carefully before purchase: This book is the international edition in mint condition with the different ISBN and book cover design, the major content is printed in full English as same as the original North American edition. The book printed in black and white, generally send in twenty-four hours after the order confirmed. All shipments go through via USPS/UPS/DHL with tracking numbers. Great professional textbook selling experience and expedite shipping service.…

Paperback. Condition: New. Machine learning is an interdisciplinary field of science and engineering that studies mathematical theories and practical applications of systems that learn. This book introduces theories, methods and applications of density ratio estimation, which is a newly emerging paradigm in the machine learning community. Various machine learning problems such as non-stationarity adaptation, outlier detection, dimensionality reduction, independent component analysis, clustering, classification and conditional density estimation can be systematically solved via the estimation of probability density ratios. The authors offer a comprehensive introduction of various density ratio estimators including methods via density estimation, moment matching, probabilistic classification, density fitting and density ratio fitting, as well as describing how these can be applied to machine learning. The book provides mathematical theories for density ratio estimation including parametric and non-parametric convergence analysis and numerical stability analysis to complete the first and definitive treatment of the entire framework of density ratio estimation in machine learning.…

Paperback. Condition: New. Variational Bayesian learning is one of the most popular methods in machine learning. Designed for researchers and graduate students in machine learning, this book summarizes recent developments in the non-asymptotic and asymptotic theory of variational Bayesian learning and suggests how this theory can be applied in practice. The authors begin by developing a basic framework with a focus on conjugacy, which enables the reader to derive tractable algorithms. Next, it summarizes non-asymptotic theory, which, although limited in application to bilinear models, precisely describes the behavior of the variational Bayesian solution and reveals its sparsity inducing mechanism. Finally, the text summarizes asymptotic theory, which reveals phase transition phenomena depending on the prior setting, thus providing suggestions on how to set hyperparameters for particular purposes. Detailed derivations allow readers to follow along without prior knowledge of the mathematical techniques specific to Bayesian learning.…

Condition: As New. Unread book in perfect condition.

Condition: New.

paperback. Condition: New.

Condition: New.

Hardcover. Condition: new. Hardcover. Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization.Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization.Standard machine learning techniques require large amounts of labeled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely difficult to collect such quantities of labeled data. In this book Masashi Sugiyama, Han Bao, Takashi Ishida, Nan Lu, Tomoya Sakai and Gang Niu present theory and algorithms for weakly supervised learning, a paradigm of machine learning from weakly labeled data. Emphasizing an approach based on empirical risk minimization and drawing on state-of-the-art research in weakly supervised learning, the book provides both the fundamentals of the field and the advanced mathematical theories underlying them. It can be used as a reference for practitioners and researchers and in the classroom.The book first mathematically formulates classification problems, defines common notations, and reviews various algorithms for supervised binary and multiclass classification. It then explores problems of binary weakly supervised classification, including positive-unlabeled (PU) classification, positive-negative-unlabeled (PNU) classification, and unlabeled-unlabeled (UU) classification. It then turns to multiclass classification, discussing complementary-label (CL) classification and partial-label (PL) classification. Finally, the book addresses more advanced issues, including a family of correction methods to improve the generalization performance of weakly supervised learning and the problem of class-prior estimation. "An overview of machine learning from data that is easily collectible, but challenging to annotate for learning algorithms"-- Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condition: New. In English.