Items related to Machine Learning: A Practical Approach on the Statistical...
Synopsis
This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and source codes. It can be used as a textbook in graduation or undergraduation courses, for self-learners, or as reference with respect to the main theoretical concepts of Machine Learning. Fundamental concepts of Linear Algebra and Optimization applied to Machine Learning are provided, as well as source codes in R, making the book as self-contained as possible.
It starts with an introduction to Machine Learning concepts and algorithms such as the Perceptron, Multilayer Perceptron and the Distance-Weighted Nearest Neighbors with examples, in order to provide the necessary foundation so the reader is able to understand the Bias-Variance Dilemma, which is the central point of the Statistical Learning Theory.
Afterwards, we introduce all assumptions and formalize the Statistical Learning Theory, allowing the practical study of different classification algorithms. Then, we proceed with concentration inequalities until arriving to the Generalization and the Large-Margin bounds, providing the main motivations for the Support Vector Machines.
From that, we introduce all necessary optimization concepts related to the implementation of Support Vector Machines. To provide a next stage of development, the book finishes with a discussion on SVM kernels as a way and motivation to study data spaces and improve classification results.
"synopsis" may belong to another edition of this title.
About the Author
riggerRodrigo Fernandes de Mello is Associate Professor with the Department of Computer Science, at the Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, SP, Brazil. He obtained his PhD degree from the University of São Paulo. His research interests include the Statistical Learning Theory, Machine Learning, Data Streams, and Applications in Dynamical Systems concepts. He has published more than 100 papers including journals and conferences, supported and organized international conferences, besides serving as Editor of International Journals.
Moacir Antonelli Ponti is Associate Professor with the Department of Computer Science, at the Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, Brazil, and was visiting researcher at the Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey. He obtained his PhD from the Federal University of São Carlos. His research interests include Pattern Recognition and Computer Vision, as well as Signal, Image and Video Processing.Review
“The book addresses the subject of machine learning, with an emphasis on statistical learning theory. ... The book can be used in ML courses as well as for independent study, since it presents very thoroughly all the fundamental theoretical insights of SLT, together with examples and implementations in R.” (Catalin Stoean, zbMATH 1408.68003, 2019)
"About this title" may belong to another edition of this title.
- PublisherSpringer
- Publication date2018
- ISBN 10 3319949888
- ISBN 13 9783319949888
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages377
Other Popular Editions of the Same Title
Search results for Machine Learning: A Practical Approach on the Statistical...
Stock Image
Machine Learning: A Practical Approach on the Statistical Learning Theory
Seller: Zubal-Books, Since 1961, Cleveland, OH, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. *FREE DOMESTIC SHIPPING until Monday, April 7* 377 pp., hardcover, new. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country. Seller Inventory # ZB1318209
Quantity: 1 available
Stock Image
Machine Learning-A Practical Approach on the Statistical Learning Theory
Seller: Romtrade Corp., STERLING HEIGHTS, MI, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide. Seller Inventory # ABTA-36986
Quantity: 5 available
Stock Image
MACHINE LEARNING-A PRACTICAL APPROACH ON THE STATISTICAL LEARNING THEORY
Seller: Books Puddle, New York, NY, U.S.A.
Seller rating 4 out of 5 stars
Condition: New. Seller Inventory # 26375701471
Quantity: 4 available
Stock Image
MACHINE LEARNING-A PRACTICAL APPROACH ON THE STATISTICAL LEARNING THEORY
Seller: Majestic Books, Hounslow, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # 370343936
Quantity: 4 available
Stock Image
MACHINE LEARNING-A PRACTICAL APPROACH ON THE STATISTICAL LEARNING THEORY
Seller: Biblios, Frankfurt am main, HESSE, Germany
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # 18375701461
Quantity: 4 available
Stock Image
Machine Learning: A Practical Approach on the Statistical Learning Theory
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # ABLIING23Mar3113020113221
Quantity: Over 20 available
Seller Image
Machine Learning
Published by
Springer International Publishing Aug 2018, 2018
ISBN 10: 3319949888
ISBN 13: 9783319949888
New
Hardcover
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and source codes. It can be used as a textbook in graduation or undergraduation courses, for self-learners, or as reference with respect to the main theoretical concepts of Machine Learning. Fundamental concepts of Linear Algebra and Optimization applied to Machine Learning are provided, as well as source codes in R, making the book as self-contained as possible.It starts with an introduction to Machine Learning concepts and algorithms such as the Perceptron, Multilayer Perceptron and the Distance-Weighted Nearest Neighbors with examples, in order to provide the necessary foundation so the reader is able to understand the Bias-Variance Dilemma, which is the central point of the Statistical Learning Theory.Afterwards, we introduce all assumptions and formalize the Statistical Learning Theory, allowing the practical study of different classification algorithms. Then, we proceed with concentration inequalities until arriving to the Generalization and the Large-Margin bounds, providing the main motivations for the Support Vector Machines. From that, we introduce all necessary optimization concepts related to the implementation of Support Vector Machines. To provide a next stage of development, the book finishes with a discussion on SVM kernels as a way and motivation to study data spaces and improve classification results. 380 pp. Englisch. Seller Inventory # 9783319949888
Quantity: 2 available
Seller Image
Machine Learning
Published by
Springer International Publishing, 2018
ISBN 10: 3319949888
ISBN 13: 9783319949888
New
Hardcover
Seller: moluna, Greven, Germany
Seller rating 5 out of 5 stars
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book includes a relevant discussion on Classification Algorithms as well as their source codes using the R Statistical LanguageIt also presents a very simple approach to understand the Statistical Learning Theory, which is considered a comple. Seller Inventory # 228331701
Quantity: Over 20 available
Seller Image
Machine Learning : A Practical Approach on the Statistical Learning Theory
Published by
Springer International Publishing, 2018
ISBN 10: 3319949888
ISBN 13: 9783319949888
New
Hardcover
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and source codes. It can be used as a textbook in graduation or undergraduation courses, for self-learners, or as reference with respect to the main theoretical concepts of Machine Learning. Fundamental concepts of Linear Algebra and Optimization applied to Machine Learning are provided, as well as source codes in R, making the book as self-contained as possible.It starts with an introduction to Machine Learning concepts and algorithms such as the Perceptron, Multilayer Perceptron and the Distance-Weighted Nearest Neighbors with examples, in order to provide the necessary foundation so the reader is able to understand the Bias-Variance Dilemma, which is the central point of the Statistical Learning Theory.Afterwards, we introduce all assumptions and formalize the Statistical Learning Theory, allowing the practical study of different classification algorithms. Then, we proceed with concentration inequalities until arriving to the Generalization and the Large-Margin bounds, providing the main motivations for the Support Vector Machines. From that, we introduce all necessary optimization concepts related to the implementation of Support Vector Machines. To provide a next stage of development, the book finishes with a discussion on SVM kernels as a way and motivation to study data spaces and improve classification results. Seller Inventory # 9783319949888
Quantity: 1 available
Stock Image
Machine Learning-A Practical Approach on the Statistical Learning Theory
Seller: UK BOOKS STORE, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
Hardcover. Condition: New. Brand New! Fast Delivery US Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-12 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability. Seller Inventory # ATL 9783319949888
Quantity: 10 available