Reverse Hypothesis Machine Learning A Practitioner's Perspective /

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 unde...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Kulkarni, Parag (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2017.
Σειρά:Intelligent Systems Reference Library, 128
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
LEADER 03489nam a22005535i 4500
001 978-3-319-55312-2
003 DE-He213
005 20170330092246.0
007 cr nn 008mamaa
008 170330s2017 gw | s |||| 0|eng d
020 |a 9783319553122  |9 978-3-319-55312-2 
024 7 |a 10.1007/978-3-319-55312-2  |2 doi 
040 |d GrThAP 
050 4 |a Q342 
072 7 |a UYQ  |2 bicssc 
072 7 |a COM004000  |2 bisacsh 
082 0 4 |a 006.3  |2 23 
100 1 |a Kulkarni, Parag.  |e author. 
245 1 0 |a Reverse Hypothesis Machine Learning  |h [electronic resource] :  |b A Practitioner's Perspective /  |c by Parag Kulkarni. 
264 1 |a Cham :  |b Springer International Publishing :  |b Imprint: Springer,  |c 2017. 
300 |a XVI, 138 p. 61 illus., 9 illus. in color.  |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 
490 1 |a Intelligent Systems Reference Library,  |x 1868-4394 ;  |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 Engineering. 
650 0 |a Knowledge management. 
650 0 |a Management. 
650 0 |a Industrial management. 
650 0 |a Computational intelligence. 
650 0 |a Machinery. 
650 0 |a Electronics. 
650 0 |a Microelectronics. 
650 1 4 |a Engineering. 
650 2 4 |a Computational Intelligence. 
650 2 4 |a Knowledge Management. 
650 2 4 |a Machinery and Machine Elements. 
650 2 4 |a Innovation/Technology Management. 
650 2 4 |a Electronics and Microelectronics, Instrumentation. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783319553115 
830 0 |a Intelligent Systems Reference Library,  |x 1868-4394 ;  |v 128 
856 4 0 |u http://dx.doi.org/10.1007/978-3-319-55312-2  |z Full Text via HEAL-Link 
912 |a ZDB-2-ENG 
950 |a Engineering (Springer-11647)