國立高雄大學圖資館 |

Language: English

Back

Machine learning foundationssupervis...

Jo, Taeho.

Machine learning foundationssupervised, unsupervised, and advanced learning /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning foundationsby Taeho Jo.
Reminder of title:	supervised, unsupervised, and advanced learning /
Author:	Jo, Taeho.
Published:	Cham :Springer International Publishing :2021.
Description:	xx, 391 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-3-030-65900-4
ISBN:	9783030659004$q(electronic bk.)

Machine learning foundationssupervised, unsupervised, and advanced learning /
Jo, Taeho.

Machine learning foundationssupervised, unsupervised, and advanced learning /[electronic resource] :by Taeho Jo. - Cham :Springer International Publishing :2021. - xx, 391 p. :ill., digital ;24 cm.

Part I. Foundation -- Chapter 1. Introduction -- Chapter 2. Numerical Vectors -- Chapter 3.Data Encoding -- Chapter 4. Simple Machine Learning Algorithms -- Part II. Supervised Learning -- Chapter 5. Instance based Learning -- Chapter 6. Probabilistic Learning -- Chapter 7. Decision Tree -- Chapter 8. Support Vector Machine -- Part III. Unsupervised Learning -- Chapter 9. Simple Clustering Algorithms -- Chapter 10. K Means Algorithm -- Chapter 11. EM Algorithm -- Chapter 12. Advanced Clustering -- Part IV. Advanced Topics -- Chapter 13. Ensemble Learning -- Chapter 14. Semi-Supervised Learning -- Chapter 15. Temporal Learning -- Chapter 16. Reinforcement Learning.

This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning. Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning; Outlines the computation paradigm for solving classification, regression, and clustering; Features essential techniques for building the a new generation of machine learning.

ISBN: 9783030659004$q(electronic bk.)

Standard No.: 10.1007/978-3-030-65900-4doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning foundationssupervised, unsupervised, and advanced learning /
LDR:02636nmm a2200325 a 4500 001 600437
003 DE-He213
005 20210520094457.0
006 m d
007 cr nn 008maaau
008 211104s2021 sz s 0 eng d
020 $a 9783030659004$q(electronic bk.)
020 $a 9783030658991$q(paper)
024 7 $a 10.1007/978-3-030-65900-4 $2 doi
035 $a 978-3-030-65900-4
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a TJK $2 bicssc
072 7 $a TEC041000 $2 bisacsh
072 7 $a TJK $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .J62 2021
100 1 $a Jo, Taeho. $3 830094
245 1 0 $a Machine learning foundations $h [electronic resource] : $b supervised, unsupervised, and advanced learning / $c by Taeho Jo.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xx, 391 p. : $b ill., digital ; $c 24 cm.
505 0 $a Part I. Foundation -- Chapter 1. Introduction -- Chapter 2. Numerical Vectors -- Chapter 3.Data Encoding -- Chapter 4. Simple Machine Learning Algorithms -- Part II. Supervised Learning -- Chapter 5. Instance based Learning -- Chapter 6. Probabilistic Learning -- Chapter 7. Decision Tree -- Chapter 8. Support Vector Machine -- Part III. Unsupervised Learning -- Chapter 9. Simple Clustering Algorithms -- Chapter 10. K Means Algorithm -- Chapter 11. EM Algorithm -- Chapter 12. Advanced Clustering -- Part IV. Advanced Topics -- Chapter 13. Ensemble Learning -- Chapter 14. Semi-Supervised Learning -- Chapter 15. Temporal Learning -- Chapter 16. Reinforcement Learning.
520 $a This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning. Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning; Outlines the computation paradigm for solving classification, regression, and clustering; Features essential techniques for building the a new generation of machine learning.
650 0 $a Machine learning. $3 188639
650 1 4 $a Communications Engineering, Networks. $3 273745
650 2 4 $a Computational Intelligence. $3 338479
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Information Storage and Retrieval. $3 274190
650 2 4 $a Big Data/Analytics. $3 742047
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-65900-4
950 $a Engineering (SpringerNature-11647)