東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Reverse hypothesis machine learning ...

Kulkarni, Parag.

Linked to FindBook

Google Book

Amazon

博客來

Reverse hypothesis machine learning = a practitioner's perspective /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Reverse hypothesis machine learning/ by Parag Kulkarni.
Reminder of title:	a practitioner's perspective /
Author:	Kulkarni, Parag.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	xvi, 138 p. :ill., digital ;24 cm.
[NT 15003449]:	Pattern Apart -- Understanding Machine Learning Opportunities -- Systemic Machine Learning -- Reinforcement and Deep Reinforcement Machine Learning -- Creative Machine Learning -- Co-operative and Collective learning for Creative Machine Learning -- Building Creative Machines with Optimal Machine Learning and Creative Machine Learning Applications -- Conclusion - Learning Continues.
Contained By:	Springer eBooks
Subject:	Machine learning. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-55312-2
ISBN:	9783319553122

Reverse hypothesis machine learning = a practitioner's perspective /
Kulkarni, Parag.

Reverse hypothesis machine learninga practitioner's perspective /[electronic resource] :by Parag Kulkarni. - Cham :Springer International Publishing :2017. - xvi, 138 p. :ill., digital ;24 cm. - Intelligent systems reference library,v.1281868-4394 ;. - Intelligent systems reference library ;v.128..

Pattern Apart -- Understanding Machine Learning Opportunities -- Systemic Machine Learning -- Reinforcement and Deep Reinforcement Machine Learning -- Creative Machine Learning -- Co-operative and Collective learning for Creative Machine Learning -- Building Creative Machines with Optimal Machine Learning and Creative Machine Learning Applications -- Conclusion - Learning Continues.

This book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and machine learning. Knowledge- acquisition -based learning is constrained by large volumes of data and is time consuming. Hence Knowledge innovation based learning is the need of time. Since under-learning results in cognitive inabilities and over-learning compromises freedom, there is need for optimal machine learning. All existing learning techniques rely on mapping input and output and establishing mathematical relationships between them. Though methods change the paradigm remains the same--the forward hypothesis machine paradigm, which tries to minimize uncertainty. The RHM, on the other hand, makes use of uncertainty for creative learning. The approach uses limited data to help identify new and surprising solutions. It focuses on improving learnability, unlike traditional approaches, which focus on accuracy. The book is useful as a reference book for machine learning researchers and professionals as well as machine intelligence enthusiasts. It can also used by practitioners to develop new machine learning applications to solve problems that require creativity.

ISBN: 9783319553122

Standard No.: 10.1007/978-3-319-55312-2doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Reverse hypothesis machine learning = a practitioner's perspective /
LDR:02576nmm a2200325 a 4500 001 2092868
003 DE-He213
005 20170915092733.0
006 m d
007 cr nn 008maaau
008 171117s2017 gw s 0 eng d
020 $a 9783319553122 $q (electronic bk.)
020 $a 9783319553115 $q (paper)
024 7 $a 10.1007/978-3-319-55312-2 $2 doi
035 $a 978-3-319-55312-2
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .K96 2017
100 1 $a Kulkarni, Parag. $3 2147015
245 1 0 $a Reverse hypothesis machine learning $h [electronic resource] : $b a practitioner's perspective / $c by Parag Kulkarni.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a xvi, 138 p. : $b ill., digital ; $c 24 cm.
490 1 $a Intelligent systems reference library, $x 1868-4394 ; $v v.128
505 0 $a Pattern Apart -- Understanding Machine Learning Opportunities -- Systemic Machine Learning -- Reinforcement and Deep Reinforcement Machine Learning -- Creative Machine Learning -- Co-operative and Collective learning for Creative Machine Learning -- Building Creative Machines with Optimal Machine Learning and Creative Machine Learning Applications -- Conclusion - Learning Continues.
520 $a This book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and machine learning. Knowledge- acquisition -based learning is constrained by large volumes of data and is time consuming. Hence Knowledge innovation based learning is the need of time. Since under-learning results in cognitive inabilities and over-learning compromises freedom, there is need for optimal machine learning. All existing learning techniques rely on mapping input and output and establishing mathematical relationships between them. Though methods change the paradigm remains the same--the forward hypothesis machine paradigm, which tries to minimize uncertainty. The RHM, on the other hand, makes use of uncertainty for creative learning. The approach uses limited data to help identify new and surprising solutions. It focuses on improving learnability, unlike traditional approaches, which focus on accuracy. The book is useful as a reference book for machine learning researchers and professionals as well as machine intelligence enthusiasts. It can also used by practitioners to develop new machine learning applications to solve problems that require creativity.
650 0 $a Machine learning. $3 533906
650 1 4 $a Engineering. $3 586835
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Knowledge Management. $3 900242
650 2 4 $a Machinery and Machine Elements. $3 893855
650 2 4 $a Innovation/Technology Management. $3 1565353
650 2 4 $a Electronics and Microelectronics, Instrumentation. $3 893838
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Intelligent systems reference library ; $v v.128. $3 3227169
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-55312-2
950 $a Engineering (Springer-11647)