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010   $a3-319-55312-7
024 7 $a10.1007/978-3-319-55312-2
035   $a(CKB)3710000001127279
035   $a(DE-He213)978-3-319-55312-2
035   $a(MiAaPQ)EBC4833900
035   $a(PPN)199767831
035   $a(EXLCZ)993710000001127279
100   $a20170330d2017      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aReverse Hypothesis Machine Learning  $eA Practitioner's Perspective /$fby Parag Kulkarni
205   $a1st ed. 2017.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017.
215   $a1 online resource (XVI, 138 p. 61 illus., 9 illus. in color.) 
225 1 $aIntelligent Systems Reference Library,$x1868-4394 ;$v128
311   $a3-319-55311-9 
320   $aIncludes bibliographical references and index.
327   $aPattern 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.
330   $aThis book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and machine learning. Knowledge- acquisition -based learning is constrained by large volumes of data and is time consuming. Hence Knowledge innovation based learning is the need of time. Since under-learning results in cognitive inabilities and over-learning compromises freedom, there is need for optimal machine learning. All existing learning techniques rely on mapping input and output and establishing mathematical relationships between them. Though methods change the paradigm remains the same?the forward hypothesis machine paradigm, which tries to minimize uncertainty. The RHM, on the other hand, makes use of uncertainty for creative learning. The approach uses limited data to help identify new and surprising solutions. It focuses on improving learnability, unlike traditional approaches, which focus on accuracy. The book is useful as a reference book for machine learning researchers and professionals as well as machine intelligence enthusiasts. It can also used by practitioners to develop new machine learning applications to solve problems that require creativity.
410  0$aIntelligent Systems Reference Library,$x1868-4394 ;$v128
606   $aComputational intelligence
606   $aKnowledge management
606   $aMachinery
606   $aManagement
606   $aIndustrial management
606   $aElectronics
606   $aMicroelectronics
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aKnowledge Management$3https://scigraph.springernature.com/ontologies/product-market-codes/515030
606   $aMachinery and Machine Elements$3https://scigraph.springernature.com/ontologies/product-market-codes/T17039
606   $aInnovation/Technology Management$3https://scigraph.springernature.com/ontologies/product-market-codes/518000
606   $aElectronics and Microelectronics, Instrumentation$3https://scigraph.springernature.com/ontologies/product-market-codes/T24027
615  0$aComputational intelligence.
615  0$aKnowledge management.
615  0$aMachinery.
615  0$aManagement.
615  0$aIndustrial management.
615  0$aElectronics.
615  0$aMicroelectronics.
615 14$aComputational Intelligence.
615 24$aKnowledge Management.
615 24$aMachinery and Machine Elements.
615 24$aInnovation/Technology Management.
615 24$aElectronics and Microelectronics, Instrumentation.
676   $a006.31
700   $aKulkarni$b Parag$4aut$4http://id.loc.gov/vocabulary/relators/aut$0845705
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910254341403321
996   $aReverse Hypothesis Machine Learning$92289757
997   $aUNINA