國立虎尾科技大學 |

Machine Learning = A Practical Approach on the Statistical Learning Theory /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Machine Learning/ by RODRIGO F MELLO, Moacir Antonelli Ponti.
其他題名:	A Practical Approach on the Statistical Learning Theory /
作者:	F MELLO, RODRIGO.
其他作者:	Antonelli Ponti, Moacir.
面頁冊數:	XV, 362 p. 190 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Artificial intelligence. -
電子資源:	https://doi.org/10.1007/978-3-319-94989-5
ISBN:	9783319949895

Machine Learning = A Practical Approach on the Statistical Learning Theory /
F MELLO, RODRIGO.

Machine LearningA Practical Approach on the Statistical Learning Theory /[electronic resource] :by RODRIGO F MELLO, Moacir Antonelli Ponti. - 1st ed. 2018. - XV, 362 p. 190 illus.online resource.

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

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

559380
Artificial intelligence.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Machine Learning = A Practical Approach on the Statistical Learning Theory /
LDR:03140nam a22003975i 4500 001 991165
003 DE-He213
005 20200703130746.0
007 cr nn 008mamaa
008 201225s2018 gw | s |||| 0|eng d
020 $a 9783319949895 $9 978-3-319-94989-5
024 7 $a 10.1007/978-3-319-94989-5 $2 doi
035 $a 978-3-319-94989-5
050 4 $a Q334-342
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
100 1 $a F MELLO, RODRIGO. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1282820
245 1 0 $a Machine Learning $h [electronic resource] : $b A Practical Approach on the Statistical Learning Theory / $c by RODRIGO F MELLO, Moacir Antonelli Ponti.
250 $a 1st ed. 2018.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a XV, 362 p. 190 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
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 Artificial intelligence. $3 559380
650 0 $a Mathematical statistics. $3 527941
650 0 $a Computer science—Mathematics. $3 1253519
650 0 $a Computer mathematics. $3 1199796
650 0 $a Statistics . $3 1253516
650 1 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Probability and Statistics in Computer Science. $3 669886
650 2 4 $a Mathematical Applications in Computer Science. $3 815331
650 2 4 $a Applied Statistics. $3 1205141
700 1 $a Antonelli Ponti, Moacir. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1282821
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319949888
776 0 8 $i Printed edition: $z 9783319949901
776 0 8 $i Printed edition: $z 9783030069490
856 4 0 $u https://doi.org/10.1007/978-3-319-94989-5
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)