Central Library

Reverse Hypothesis Machine Learning (Record no. 79825)

000 -LEADER
fixed length control field 03759nam a22005895i 4500
001 - CONTROL NUMBER
control field 978-3-319-55312-2
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220801221600.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 170330s2017 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783319553122
-- 978-3-319-55312-2
082 04 - CLASSIFICATION NUMBER
Call Number 006.3
100 1# - AUTHOR NAME
Author Kulkarni, Parag.
245 10 - TITLE STATEMENT
Title Reverse Hypothesis Machine Learning
Sub Title A Practitioner's Perspective /
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2017.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XVI, 138 p. 61 illus., 9 illus. in color.
490 1# - SERIES STATEMENT
Series statement Intelligent Systems Reference Library,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 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 ## - SUMMARY, ETC.
Summary, etc 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.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-319-55312-2
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2017.
336 ## -
-- text
-- txt
-- rdacontent
337 ## -
-- computer
-- c
-- rdamedia
338 ## -
-- online resource
-- cr
-- rdacarrier
347 ## -
-- text file
-- PDF
-- rda
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Knowledge management.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Machinery.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Technological innovations.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Electronics.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Knowledge Management.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Machinery and Machine Elements.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Innovation and Technology Management.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Electronics and Microelectronics, Instrumentation.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 1868-4408 ;
912 ## -
-- ZDB-2-ENG
912 ## -
-- ZDB-2-SXE

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