國立高雄大學圖資館 |

Language: English

Back

Machine learninga practical approach...

Mello, Rodrigo Fernandes de.

Machine learninga practical approach on the statistical learning theory /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learningby Rodrigo Fernandes de Mello, Moacir Antonelli Ponti.
Reminder of title:	a practical approach on the statistical learning theory /
Author:	Mello, Rodrigo Fernandes de.
other author:	Ponti, Moacir Antonelli.
Published:	Cham :Springer International Publishing :2018.
Description:	xv, 362 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learning.
Online resource:	http://dx.doi.org/10.1007/978-3-319-94989-5
ISBN:	9783319949895$q(electronic bk.)

Machine learninga practical approach on the statistical learning theory /
Mello, Rodrigo Fernandes de.

Machine learninga practical approach on the statistical learning theory /[electronic resource] :by Rodrigo Fernandes de Mello, Moacir Antonelli Ponti. - Cham :Springer International Publishing :2018. - xv, 362 p. :ill., digital ;24 cm.

Chapter 1 - A Brief Review on Machine Learning -- Chapter 2 - Statistical Learning Theory -- Chapter 3 - Assessing Learning Algorithms -- Chapter 4 - Introduction to Support Vector Machines -- Chapter 5 - In Search for the Optimization Algorithm -- Chapter 6 - A Brief Introduction on Kernels.

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.

ISBN: 9783319949895$q(electronic bk.)

Standard No.: 10.1007/978-3-319-94989-5doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learninga practical approach on the statistical learning theory /
LDR:02777nmm a2200325 a 4500 001 542783
003 DE-He213
005 20180801182859.0
006 m d
007 cr nn 008maaau
008 190411s2018 gw s 0 eng d
020 $a 9783319949895$q(electronic bk.)
020 $a 9783319949888$q(paper)
024 7 $a 10.1007/978-3-319-94989-5 $2 doi
035 $a 978-3-319-94989-5
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UYQ $2 bicssc
072 7 $a TJFM1 $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .M527 2018
100 1 $a Mello, Rodrigo Fernandes de. $3 820690
245 1 0 $a Machine learning $h [electronic resource] : $b a practical approach on the statistical learning theory / $c by Rodrigo Fernandes de Mello, Moacir Antonelli Ponti.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a xv, 362 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1 - A Brief Review on Machine Learning -- Chapter 2 - Statistical Learning Theory -- Chapter 3 - Assessing Learning Algorithms -- Chapter 4 - Introduction to Support Vector Machines -- Chapter 5 - In Search for the Optimization Algorithm -- Chapter 6 - A Brief Introduction on Kernels.
520 $a 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.
650 0 $a Machine learning. $3 188639
650 1 4 $a Computer Science. $3 212513
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 252959
650 2 4 $a Probability and Statistics in Computer Science. $3 274053
650 2 4 $a Mathematical Applications in Computer Science. $3 530811
650 2 4 $a Applied Statistics. $3 805583
700 1 $a Ponti, Moacir Antonelli. $3 820691
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-94989-5
950 $a Computer Science (Springer-11645)