國立高雄大學圖資館 |

Language: English

Back

Machine Learning Models and Algorith...

SpringerLink (Online service)

Machine Learning Models and Algorithms for Big Data ClassificationThinking with Examples for Effective Learning /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning Models and Algorithms for Big Data Classificationby Shan Suthaharan.
Reminder of title:	Thinking with Examples for Effective Learning /
Author:	Suthaharan, Shan.
Published:	Boston, MA :Springer US :2016.
Description:	xix, 359 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learning.
Online resource:	http://dx.doi.org/10.1007/978-1-4899-7641-3
ISBN:	9781489976413$q(electronic bk.)

Machine Learning Models and Algorithms for Big Data ClassificationThinking with Examples for Effective Learning /
Suthaharan, Shan.

Machine Learning Models and Algorithms for Big Data ClassificationThinking with Examples for Effective Learning /[electronic resource] :by Shan Suthaharan. - Boston, MA :Springer US :2016. - xix, 359 p. :ill., digital ;24 cm. - Integrated series in information systems,v.361571-0270 ;. - Integrated series in information systems ;v.28..

Science of Information -- Part I Understanding Big Data -- Big Data Essentials -- Big Data Analytics -- Part II Understanding Big Data Systems -- Distributed File System -- MapReduce Programming Platform -- Part III Understanding Machine Learning -- Modeling and Algorithms -- Supervised Learning Models -- Supervised Learning Algorithms -- Support Vector Machine -- Decision Tree Learning -- Part IV Understanding Scaling-Up Machine Learning -- Random Forest Learning -- Deep Learning Models -- Chandelier Decision Tree -- Dimensionality Reduction.

This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems.

ISBN: 9781489976413$q(electronic bk.)

Standard No.: 10.1007/978-1-4899-7641-3doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .S88 2016

Dewey Class. No.: 006.31

Machine Learning Models and Algorithms for Big Data ClassificationThinking with Examples for Effective Learning /
LDR:03599nmm a2200325 a 4500 001 481229
003 DE-He213
005 20160722095120.0
006 m d
007 cr nn 008maaau
008 161007s2016 mau s 0 eng d
020 $a 9781489976413$q(electronic bk.)
020 $a 9781489976406$q(paper)
024 7 $a 10.1007/978-1-4899-7641-3 $2 doi
035 $a 978-1-4899-7641-3
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .S88 2016
072 7 $a KJM $2 bicssc
072 7 $a BUS041000 $2 bisacsh
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .S966 2016
100 1 $a Suthaharan, Shan. $3 737103
245 1 0 $a Machine Learning Models and Algorithms for Big Data Classification $h [electronic resource] : $b Thinking with Examples for Effective Learning / $c by Shan Suthaharan.
260 $a Boston, MA : $b Springer US : $b Imprint: Springer, $c 2016.
300 $a xix, 359 p. : $b ill., digital ; $c 24 cm.
490 1 $a Integrated series in information systems, $x 1571-0270 ; $v v.36
505 0 $a Science of Information -- Part I Understanding Big Data -- Big Data Essentials -- Big Data Analytics -- Part II Understanding Big Data Systems -- Distributed File System -- MapReduce Programming Platform -- Part III Understanding Machine Learning -- Modeling and Algorithms -- Supervised Learning Models -- Supervised Learning Algorithms -- Support Vector Machine -- Decision Tree Learning -- Part IV Understanding Scaling-Up Machine Learning -- Random Forest Learning -- Deep Learning Models -- Chandelier Decision Tree -- Dimensionality Reduction.
520 $a This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems.
650 0 $a Machine learning. $3 188639
650 0 $a Big data. $3 609582
650 0 $a Electronic data processing. $3 201945
650 0 $a Machine theory. $3 199330
650 1 4 $a Business and Management. $2 eflch $3 639169
650 2 4 $a Management. $3 180005
650 2 4 $a Database Management. $3 273994
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 252959
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Integrated series in information systems ; $v v.28. $3 557577
856 4 0 $u http://dx.doi.org/10.1007/978-1-4899-7641-3
950 $a Business and Management (Springer-41169)