LEADER 06262nam 22008295 450 001 996465304403316 005 20230401012132.0 010 $a1-280-38579-0 010 $a9786613563712 010 $a3-642-11928-X 024 7 $a10.1007/978-3-642-11928-6 035 $a(CKB)2560000000009126 035 $a(SSID)ssj0000399591 035 $a(PQKBManifestationID)11286505 035 $a(PQKBTitleCode)TC0000399591 035 $a(PQKBWorkID)10385216 035 $a(PQKB)10381913 035 $a(DE-He213)978-3-642-11928-6 035 $a(MiAaPQ)EBC3065171 035 $a(PPN)149073607 035 $a(EXLCZ)992560000000009126 100 $a20100407d2010 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aFormal Concept Analysis$b[electronic resource] $e8th International Conference, ICFCA 2010, Agadir, Morocco, March 15-18, 2010, Procedings /$fedited by Léonard Kwuida, Baris Sertkaya 205 $a1st ed. 2010. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2010. 215 $a1 online resource (XII, 340 p. 91 illus.) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v5986 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-642-11927-1 320 $aIncludes bibliographical references and index. 327 $aInvited Talks -- About the Enumeration Algorithms of Closed Sets -- Mathematics Presenting, Reflecting, Judging -- The Role of Concept, Context, and Component for Dependable Software Development -- Statistical Methods for Data Mining and Knowledge Discovery -- Regular Contributions -- Formal Concept Analysis of Two-Dimensional Convex Continuum Structures -- Counting of Moore Families for n=7 -- Lattice Drawings and Morphisms -- Approximations in Concept Lattices -- Hardness of Enumerating Pseudo-intents in the Lectic Order -- On Links between Concept Lattices and Related Complexity Problems -- An Algorithm for Extracting Rare Concepts with Concise Intents -- Conditional Functional Dependencies: An FCA Point of View -- Constrained Closed Datacubes -- Conceptual Navigation in RDF Graphs with SPARQL-Like Queries -- An Approach to Exploring Description Logic Knowledge Bases -- On Categorial Grammars as Logical Information Systems -- Describing Role Models in Terms of Formal Concept Analysis -- Approaches to the Selection of Relevant Concepts in the Case of Noisy Data -- Concept Analysis as a Framework for Mining Functional Features from Legacy Code -- Concept Neighbourhoods in Lexical Databases -- A Survey of Hybrid Representations of Concept Lattices in Conceptual Knowledge Processing -- History -- Two Basic Algorithms in Concept Analysis. 330 $aThis volume contains selected papers presented at ICFCA 2010, the 8th Int- national Conference on Formal Concept Analysis. The ICFCA conference series aims to be the prime forum for dissemination of advances in applied lattice and order theory, and in particular advances in theory and applications of Formal Concept Analysis. Formal Concept Analysis (FCA) is a ?eld of applied mathematics with its mathematical root in order theory, in particular the theory of complete lattices. Researchershadlongbeenawareofthefactthatthese?eldshavemanypotential applications.FCAemergedinthe1980sfrome?ortstorestructurelattice theory to promote better communication between lattice theorists and potential users of lattice theory. The key theme was the mathematical formalization of c- cept and conceptual hierarchy. Since then, the ?eld has developed into a growing research area in its own right with a thriving theoretical community and an - creasingnumberofapplicationsindataandknowledgeprocessingincludingdata visualization, information retrieval, machine learning, sofware engineering, data analysis, data mining in Web 2.0, analysis of social networks, concept graphs, contextual logic and description logics. ICFCA 2010 took place during March 15?18, 2010 in Agadir, Morocco. We received 37 high-quality submissions out of which 17 were chosen as regular papers in these proceedings after a competitive selection process. Less mature works that were still considered valuable for discussion at the conference were collected in the supplementary proceedings. The papers in the present volume coveradvancesinvariousaspectsofFCArangingfromitstheoreticalfoundations to its applications in numerous other ?elds. In addition to the regular papers, thisvolumealsocontainsfourkeynotepapersarisingfromtheseveninvitedtalks given at the conference. We are also delighted to include a reprint of Bernhard Ganter?sseminalpaper on hiswell-knownalgorithmfor enumerating closedsets. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v5986 606 $aAlgebra 606 $aArtificial intelligence 606 $aMathematical models 606 $aData mining 606 $aMachine theory 606 $aComputer science?Mathematics 606 $aDiscrete mathematics 606 $aAlgebra 606 $aArtificial Intelligence 606 $aMathematical Modeling and Industrial Mathematics 606 $aData Mining and Knowledge Discovery 606 $aFormal Languages and Automata Theory 606 $aDiscrete Mathematics in Computer Science 615 0$aAlgebra. 615 0$aArtificial intelligence. 615 0$aMathematical models. 615 0$aData mining. 615 0$aMachine theory. 615 0$aComputer science?Mathematics. 615 0$aDiscrete mathematics. 615 14$aAlgebra. 615 24$aArtificial Intelligence. 615 24$aMathematical Modeling and Industrial Mathematics. 615 24$aData Mining and Knowledge Discovery. 615 24$aFormal Languages and Automata Theory. 615 24$aDiscrete Mathematics in Computer Science. 676 $a512 702 $aKwuida$b Léonard$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSertkaya$b Baris$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aInternational Conference on Formal Concept Analysis 906 $aBOOK 912 $a996465304403316 996 $aFormal Concept Analysis$9772457 997 $aUNISA LEADER 04512nam 22007575 450 001 996465401303316 005 20230329175138.0 010 $a3-319-46687-9 024 7 $a10.1007/978-3-319-46687-3 035 $a(CKB)3710000000872960 035 $a(DE-He213)978-3-319-46687-3 035 $a(MiAaPQ)EBC6282911 035 $a(MiAaPQ)EBC5576489 035 $a(Au-PeEL)EBL5576489 035 $a(OCoLC)960195458 035 $a(PPN)195511352 035 $a(EXLCZ)993710000000872960 100 $a20160928d2016 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNeural Information Processing$b[electronic resource] $e23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16?21, 2016, Proceedings, Part I /$fedited by Akira Hirose, Seiichi Ozawa, Kenji Doya, Kazushi Ikeda, Minho Lee, Derong Liu 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (XIX, 639 p. 250 illus.) 225 1 $aTheoretical Computer Science and General Issues,$x2512-2029 ;$v9947 311 $a3-319-46686-0 327 $aDeep and reinforcement learning -- Big data analysis -- Neural data analysis.-Robotics and control -- Bio-inspired/energy efficient information processing.-Whole brain architecture -- Neurodynamics -- Bioinformatics -- Biomedical engineering -- Data mining and cybersecurity workshop -- Machine learning -- Neuromorphic hardware -- Sensory perception -- Pattern recognition -- Social networks -- Brain-machine interface -- Computer vision -- Time series analysis.-Data-driven approach for extracting latent features -- Topological and graph based clustering methods -- Computational intelligence -- Data mining -- Deep neural networks -- Computational and cognitive neurosciences -- Theory and algorithms. 330 $aThe four volume set LNCS 9947, LNCS 9948, LNCS 9949, and LNCS 9950 constitutes the proceedings of the 23rd International Conference on Neural Information Processing, ICONIP 2016, held in Kyoto, Japan, in October 2016. The 296 full papers presented were carefully reviewed and selected from 431 submissions. The 4 volumes are organized in topical sections on deep and reinforcement learning; big data analysis; neural data analysis; robotics and control; bio-inspired/energy efficient information processing; whole brain architecture; neurodynamics; bioinformatics; biomedical engineering; data mining and cybersecurity workshop; machine learning; neuromorphic hardware; sensory perception; pattern recognition; social networks; brain-machine interface; computer vision; time series analysis; data-driven approach for extracting latent features; topological and graph based clustering methods; computational intelligence; data mining; deep neural networks; computational and cognitive neurosciences; theory and algorithms. 410 0$aTheoretical Computer Science and General Issues,$x2512-2029 ;$v9947 606 $aPattern recognition systems 606 $aComputer vision 606 $aArtificial intelligence 606 $aComputer science 606 $aData mining 606 $aAutomated Pattern Recognition 606 $aComputer Vision 606 $aArtificial Intelligence 606 $aTheory of Computation 606 $aData Mining and Knowledge Discovery 615 0$aPattern recognition systems. 615 0$aComputer vision. 615 0$aArtificial intelligence. 615 0$aComputer science. 615 0$aData mining. 615 14$aAutomated Pattern Recognition. 615 24$aComputer Vision. 615 24$aArtificial Intelligence. 615 24$aTheory of Computation. 615 24$aData Mining and Knowledge Discovery. 676 $a006.32 702 $aHirose$b Akira$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aOzawa$b Seiichi$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aDoya$b Kenji$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aIkeda$b Kazushi$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLee$b Minho$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLiu$b Derong$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465401303316 996 $aNeural Information Processing$92554499 997 $aUNISA