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Feature Extraction - Foundations and Applications (Hardcover): Isabelle Guyon, Steve Gunn, Masoud Nikravesh, Lofti A. Zadeh Feature Extraction - Foundations and Applications (Hardcover)
Isabelle Guyon, Steve Gunn, Masoud Nikravesh, Lofti A. Zadeh
R8,460 Discovery Miles 84 600 Ships in 10 - 15 working days

This book is both a reference for engineers and scientists and a teaching resource, featuring tutorial chapters and research papers on feature extraction. Its CD-ROM includes the data of the NIPS 2003 Feature Selection Challenge and sample Matlab code. Until now there has been insufficient consideration of feature selection algorithms, no unified presentation of leading methods, and no systematic comparisons.

Gesture Recognition (Hardcover, 1st ed. 2017): Sergio Escalera, Isabelle Guyon, Vassilis Athitsos Gesture Recognition (Hardcover, 1st ed. 2017)
Sergio Escalera, Isabelle Guyon, Vassilis Athitsos
R4,518 Discovery Miles 45 180 Ships in 10 - 15 working days

This book presents a selection of chapters, written by leading international researchers, related to the automatic analysis of gestures from still images and multi-modal RGB-Depth image sequences. It offers a comprehensive review of vision-based approaches for supervised gesture recognition methods that have been validated by various challenges. Several aspects of gesture recognition are reviewed, including data acquisition from different sources, feature extraction, learning, and recognition of gestures.

Cause Effect Pairs in Machine Learning (Hardcover, 1st ed. 2019): Isabelle Guyon, Alexander Statnikov, Berna Bakir Batu Cause Effect Pairs in Machine Learning (Hardcover, 1st ed. 2019)
Isabelle Guyon, Alexander Statnikov, Berna Bakir Batu
R3,912 Discovery Miles 39 120 Ships in 12 - 19 working days

This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect ("Does altitude cause a change in atmospheric pressure, or vice versa?") is here cast as a binary classification problem, to be tackled by machine learning algorithms. Based on the results of the ChaLearn Cause-Effect Pairs Challenge, this book reveals that the joint distribution of two variables can be scrutinized by machine learning algorithms to reveal the possible existence of a "causal mechanism", in the sense that the values of one variable may have been generated from the values of the other. This book provides both tutorial material on the state-of-the-art on cause-effect pairs and exposes the reader to more advanced material, with a collection of selected papers. Supplemental material includes videos, slides, and code which can be found on the workshop website. Discovering causal relationships from observational data will become increasingly important in data science with the increasing amount of available data, as a means of detecting potential triggers in epidemiology, social sciences, economy, biology, medicine, and other sciences.

Feature Extraction - Foundations and Applications (Paperback, Softcover reprint of the original 1st ed. 2006): Isabelle Guyon,... Feature Extraction - Foundations and Applications (Paperback, Softcover reprint of the original 1st ed. 2006)
Isabelle Guyon, Steve Gunn, Masoud Nikravesh, Lofti A. Zadeh
R8,511 Discovery Miles 85 110 Ships in 10 - 15 working days

This book is both a reference for engineers and scientists and a teaching resource, featuring tutorial chapters and research papers on feature extraction. Until now there has been insufficient consideration of feature selection algorithms, no unified presentation of leading methods, and no systematic comparisons.

Cause Effect Pairs in Machine Learning (Paperback, 1st ed. 2019): Isabelle Guyon, Alexander Statnikov, Berna Bakir Batu Cause Effect Pairs in Machine Learning (Paperback, 1st ed. 2019)
Isabelle Guyon, Alexander Statnikov, Berna Bakir Batu
R2,901 Discovery Miles 29 010 Ships in 10 - 15 working days

This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect ("Does altitude cause a change in atmospheric pressure, or vice versa?") is here cast as a binary classification problem, to be tackled by machine learning algorithms. Based on the results of the ChaLearn Cause-Effect Pairs Challenge, this book reveals that the joint distribution of two variables can be scrutinized by machine learning algorithms to reveal the possible existence of a "causal mechanism", in the sense that the values of one variable may have been generated from the values of the other. This book provides both tutorial material on the state-of-the-art on cause-effect pairs and exposes the reader to more advanced material, with a collection of selected papers. Supplemental material includes videos, slides, and code which can be found on the workshop website. Discovering causal relationships from observational data will become increasingly important in data science with the increasing amount of available data, as a means of detecting potential triggers in epidemiology, social sciences, economy, biology, medicine, and other sciences.

Gentle Introduction To Support Vector Machines In Biomedicine, A - Volume 2: Case Studies And Benchmarks (Hardcover): Alexander... Gentle Introduction To Support Vector Machines In Biomedicine, A - Volume 2: Case Studies And Benchmarks (Hardcover)
Alexander Statnikov, Constantin F. Aliferis, Douglas P. Hardin, Isabelle Guyon
R2,085 Discovery Miles 20 850 Ships in 12 - 19 working days

Support Vector Machines (SVMs) are among the most important recent developments in pattern recognition and statistical machine learning. They have found a great range of applications in various fields including biology and medicine. However, biomedical researchers often experience difficulties grasping both the theory and applications of these important methods because of lack of technical background. The purpose of this book is to introduce SVMs and their extensions and allow biomedical researchers to understand and apply them in real-life research in a very easy manner. The book is to consist of two volumes: theory and methods (Volume 1) and case studies (Volume 2).

Gentle Introduction To Support Vector Machines In Biomedicine, A - Volume 1: Theory And Methods (Hardcover): Alexander... Gentle Introduction To Support Vector Machines In Biomedicine, A - Volume 1: Theory And Methods (Hardcover)
Alexander Statnikov, Constantin F. Aliferis, Douglas P. Hardin, Isabelle Guyon
R2,201 Discovery Miles 22 010 Ships in 12 - 19 working days

Support Vector Machines (SVMs) are among the most important recent developments in pattern recognition and statistical machine learning. They have found a great range of applications in various fields including biology and medicine. However, biomedical researchers often experience difficulties grasping both the theory and applications of these important methods because of lack of technical background. The purpose of this book is to introduce SVMs and their extensions and allow biomedical researchers to understand and apply them in real-life research in a very easy manner. The book is to consist of two volumes: theory and methods (Volume 1) and case studies (Volume 2).

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