جهت دسترسی به کاربرگه ی زیر، از این لینک استفاده کنید. http://localhost/handle/Hannan/1274
نمایش کامل اطلاعات کتابشناختی
فیلد DublinCoreمقدار زبان
dc.contributor.authorLachiche, Nicolas. ;en_US
dc.contributor.authorVrain, Christel. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:27:07Z-
dc.date.available2020-05-17T08:27:07Z-
dc.date.issued2018en_US
dc.identifier.isbn9783319780900 ;en_US
dc.identifier.isbn9783319780894 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/1274-
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionPrinted edition: ; 9783319780894. ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractThis book constitutes the thoroughly refereed post-conference proceedings of the 27th International Conference on Inductive Logic Programming, ILP 2017, held in Orleans, France, in September 2017. The 12 full papers presented were carefully reviewed and selected from numerous submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data. ;en_US
dc.description.statementofresponsibilityedited by Nicolas Lachiche, Christel Vrain.en_US
dc.description.tableofcontentsRelational Affordance Learning for Task-dependent Robot Grasping -- Positive and Unlabeled Relational Classification Through Label Frequency Estimation -- On Applying Probabilistic Logic Programming to Breast Cancer Data -- Logical Vision: One-Shot Meta-Interpretive Learning from Real Images -- Demystifying Relational Latent Representations -- Parallel Online Learning of Event Definitions -- Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach -- Parallel Inductive Logic Programming System for Super-linear Speedup -- Inductive Learning from State Transitions over Continuous Domains -- Stacked Structure Learning for Lifted Relational Neural Networks -- Pruning Hypothesis Spaces Using Learned Domain Theories -- An Investigation into the Role of Domain-knowledge on the Use of Embeddings. ;en_US
dc.format.extentX, 185 p. 74 illus. ; online resource. ;en_US
dc.publisherSpringer International Publishing :en_US
dc.publisherImprint: Springer,en_US
dc.relation.ispartofseriesLecture Notes in Computer Science, ; 0302-9743 ; ; 10759. ;en_US
dc.relation.ispartofseriesLecture Notes in Computer Science, ; 0302-9743 ; ; 10759. ;en_US
dc.relation.haspart9783319780894.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectComputer Programmingen_US
dc.subjectProgramming Languages and Electronic Computersen_US
dc.subjectComputer logic. ;en_US
dc.subjectMathematical logic. ;en_US
dc.subjectArtificial Intelligenceen_US
dc.subjectComputer Scienceen_US
dc.subjectMathematical Logic and Formal Languages. ;en_US
dc.subjectArtificial Intelligence and Roboticsen_US
dc.subjectProgramming Languages and Compilers and Interpretersen_US
dc.subjectLogics and Meanings of Programs. ;en_US
dc.subjectProgramming Techniquesen_US
dc.subject.ddc005.131 ; 23 ;en_US
dc.subject.lccQA8.9-QA10.3 ;en_US
dc.titleInductive Logic Programmingen_US
dc.title.alternative27th International Conference, ILP 2017, Orleans, France, September 4-6, 2017, Revised Selected Papers /en_US
dc.typeBooken_US
dc.publisher.placeCham :en_US
مجموعه(های):مدیریت فناوری اطلاعات

پیوست های این کاربرگه
فایل توضیحات اندازهفرمت  
9783319780894.pdf14.53 MBAdobe PDFتصویر
مشاهده فایل
نمایش کامل اطلاعات کتابشناختی
فیلد DublinCoreمقدار زبان
dc.contributor.authorLachiche, Nicolas. ;en_US
dc.contributor.authorVrain, Christel. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:27:07Z-
dc.date.available2020-05-17T08:27:07Z-
dc.date.issued2018en_US
dc.identifier.isbn9783319780900 ;en_US
dc.identifier.isbn9783319780894 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/1274-
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionPrinted edition: ; 9783319780894. ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractThis book constitutes the thoroughly refereed post-conference proceedings of the 27th International Conference on Inductive Logic Programming, ILP 2017, held in Orleans, France, in September 2017. The 12 full papers presented were carefully reviewed and selected from numerous submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data. ;en_US
dc.description.statementofresponsibilityedited by Nicolas Lachiche, Christel Vrain.en_US
dc.description.tableofcontentsRelational Affordance Learning for Task-dependent Robot Grasping -- Positive and Unlabeled Relational Classification Through Label Frequency Estimation -- On Applying Probabilistic Logic Programming to Breast Cancer Data -- Logical Vision: One-Shot Meta-Interpretive Learning from Real Images -- Demystifying Relational Latent Representations -- Parallel Online Learning of Event Definitions -- Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach -- Parallel Inductive Logic Programming System for Super-linear Speedup -- Inductive Learning from State Transitions over Continuous Domains -- Stacked Structure Learning for Lifted Relational Neural Networks -- Pruning Hypothesis Spaces Using Learned Domain Theories -- An Investigation into the Role of Domain-knowledge on the Use of Embeddings. ;en_US
dc.format.extentX, 185 p. 74 illus. ; online resource. ;en_US
dc.publisherSpringer International Publishing :en_US
dc.publisherImprint: Springer,en_US
dc.relation.ispartofseriesLecture Notes in Computer Science, ; 0302-9743 ; ; 10759. ;en_US
dc.relation.ispartofseriesLecture Notes in Computer Science, ; 0302-9743 ; ; 10759. ;en_US
dc.relation.haspart9783319780894.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectComputer Programmingen_US
dc.subjectProgramming Languages and Electronic Computersen_US
dc.subjectComputer logic. ;en_US
dc.subjectMathematical logic. ;en_US
dc.subjectArtificial Intelligenceen_US
dc.subjectComputer Scienceen_US
dc.subjectMathematical Logic and Formal Languages. ;en_US
dc.subjectArtificial Intelligence and Roboticsen_US
dc.subjectProgramming Languages and Compilers and Interpretersen_US
dc.subjectLogics and Meanings of Programs. ;en_US
dc.subjectProgramming Techniquesen_US
dc.subject.ddc005.131 ; 23 ;en_US
dc.subject.lccQA8.9-QA10.3 ;en_US
dc.titleInductive Logic Programmingen_US
dc.title.alternative27th International Conference, ILP 2017, Orleans, France, September 4-6, 2017, Revised Selected Papers /en_US
dc.typeBooken_US
dc.publisher.placeCham :en_US
مجموعه(های):مدیریت فناوری اطلاعات

پیوست های این کاربرگه
فایل توضیحات اندازهفرمت  
9783319780894.pdf14.53 MBAdobe PDFتصویر
مشاهده فایل
نمایش کامل اطلاعات کتابشناختی
فیلد DublinCoreمقدار زبان
dc.contributor.authorLachiche, Nicolas. ;en_US
dc.contributor.authorVrain, Christel. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:27:07Z-
dc.date.available2020-05-17T08:27:07Z-
dc.date.issued2018en_US
dc.identifier.isbn9783319780900 ;en_US
dc.identifier.isbn9783319780894 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/1274-
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionPrinted edition: ; 9783319780894. ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractThis book constitutes the thoroughly refereed post-conference proceedings of the 27th International Conference on Inductive Logic Programming, ILP 2017, held in Orleans, France, in September 2017. The 12 full papers presented were carefully reviewed and selected from numerous submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data. ;en_US
dc.description.statementofresponsibilityedited by Nicolas Lachiche, Christel Vrain.en_US
dc.description.tableofcontentsRelational Affordance Learning for Task-dependent Robot Grasping -- Positive and Unlabeled Relational Classification Through Label Frequency Estimation -- On Applying Probabilistic Logic Programming to Breast Cancer Data -- Logical Vision: One-Shot Meta-Interpretive Learning from Real Images -- Demystifying Relational Latent Representations -- Parallel Online Learning of Event Definitions -- Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach -- Parallel Inductive Logic Programming System for Super-linear Speedup -- Inductive Learning from State Transitions over Continuous Domains -- Stacked Structure Learning for Lifted Relational Neural Networks -- Pruning Hypothesis Spaces Using Learned Domain Theories -- An Investigation into the Role of Domain-knowledge on the Use of Embeddings. ;en_US
dc.format.extentX, 185 p. 74 illus. ; online resource. ;en_US
dc.publisherSpringer International Publishing :en_US
dc.publisherImprint: Springer,en_US
dc.relation.ispartofseriesLecture Notes in Computer Science, ; 0302-9743 ; ; 10759. ;en_US
dc.relation.ispartofseriesLecture Notes in Computer Science, ; 0302-9743 ; ; 10759. ;en_US
dc.relation.haspart9783319780894.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectComputer Programmingen_US
dc.subjectProgramming Languages and Electronic Computersen_US
dc.subjectComputer logic. ;en_US
dc.subjectMathematical logic. ;en_US
dc.subjectArtificial Intelligenceen_US
dc.subjectComputer Scienceen_US
dc.subjectMathematical Logic and Formal Languages. ;en_US
dc.subjectArtificial Intelligence and Roboticsen_US
dc.subjectProgramming Languages and Compilers and Interpretersen_US
dc.subjectLogics and Meanings of Programs. ;en_US
dc.subjectProgramming Techniquesen_US
dc.subject.ddc005.131 ; 23 ;en_US
dc.subject.lccQA8.9-QA10.3 ;en_US
dc.titleInductive Logic Programmingen_US
dc.title.alternative27th International Conference, ILP 2017, Orleans, France, September 4-6, 2017, Revised Selected Papers /en_US
dc.typeBooken_US
dc.publisher.placeCham :en_US
مجموعه(های):مدیریت فناوری اطلاعات

پیوست های این کاربرگه
فایل توضیحات اندازهفرمت  
9783319780894.pdf14.53 MBAdobe PDFتصویر
مشاهده فایل