03008nam 2200529 450 991079371260332120220531014705.03-657-79252-X10.30965/9783657792528(CKB)4100000008965698(OCoLC)1110055281(nllekb)BRILL9783657792528(MiAaPQ)EBC6516960(Au-PeEL)EBL6516960(OCoLC)1243553830(Brill | Schöningh)9783657792528(EXLCZ)99410000000896569820220531d2019 uy 0gerurun####uuuuatxtrdacontentcrdamediardacarrierVom Stahlkonzern zum Firmenverbund die Unternehmen Heinrich Thyssen-Bornemiszas von 1926 bis 1932 /Harald Wixforth1st ed.Paderborn, Germany :Ferdinand Schöningh,[2019]©20191 online resourceFamilie - Unternehmen - Öffentlichkeit: Thyssen im 20. Jahrhundert3-506-79252-0 Includes bibliographical references (pages [254]-262) and index.Preliminary Material -- Editorial -- Einleitung -- Am Scheideweg -- Auf der Suche nach neuen Strukturen -- Unter neuen Bedingungen – die ENTWICKLUNG Einzelner Verbundsparten -- Vom Stahlkonzern zum Firmenverbund – Herausforderung gemeistert? -- Organigramm des Thyssen-Bornemisza-Komplexes (alt), Stand 1940 -- Die Entwicklung wichtiger Bilanzposten ausgewählter Verbundfirmen -- Verzeichnis der Abbildungen -- Abkürzungen -- Quellen und Literatur -- Danksagung -- Personenregister -- Reihenübersicht.Nach dem Tod August Thyssens versuchte sein Sohn Heinrich Thyssen-Bornemisza, als Unternehmer eigene Wege zu gehen. Die Entwicklung seiner Firmen ist bis heute ein kaum bekannter Teil der Geschichte Thyssens im 20. Jahrhundert. Ab Herbst 1926 verfolgte Heinrich Thyssen-Bornemizsa das Ziel, seinen Erbteil des Thyssen-Konzerns effizient zusammenzufassen. Der von ihm gegründeten Holding gelang es jedoch nicht, ihre Aufgabe als zentrales Lenkungs- und Kontrollorgan zu erfüllen. Die Studie untersucht die Gründe dafür. Zudem beleuchtet sie die Entwicklung und Wettbewerbsposition einzelner Betriebe aus Heinrichs Verbund bis zum Ende seiner Formationsphase 1932 und erklärt, warum sich diese trotz wirtschaftlich schwieriger Rahmenbedingungen erfolgreich am Markt behaupten konnten.Familie - Unternehmen - Öffentlichkeit: Thyssen im 20. Jahrhundert ;9.Steel industry and tradeGermanyHistoryGermanyfastHistory.fastSteel industry and tradeHistory.338.76691092Wixforth Harald144790MiAaPQMiAaPQMiAaPQBOOK9910793712603321Vom Stahlkonzern zum Firmenverbund3789262UNINA01049nam0 22002771i 450 UON0032473620231205104155.452978-88-220-6807-120090623d2009 |0itac50 baitaIT|||| |||||ˆI ‰nemici della scienzaintegralismi filosofici, religiosi e ambientalistiSilvano Fusoprefazione di Umberto VeronesiBariEdizioni Dedalo2009295 p.21 cm.Filosofia della scienzaUONC053631FIITBariUONL000072FUSOSilvanoUONV185842481336VERONESIUmbertoUONV108740DedaloUONV248403650ITSOL20250228RICASIBA - SISTEMA BIBLIOTECARIO DI ATENEOUONSIUON00324736SIBA - SISTEMA BIBLIOTECARIO DI ATENEOSI A 0915 SI SC 45484 5 0915 BuonoNemici della scienza1369936UNIOR05449nam 2200817Ia 450 991095339710332120251116203733.097866119190549781281919052128191905597898127702029812770208(CKB)1000000000412081(EBL)1193191(SSID)ssj0000290382(PQKBManifestationID)11225459(PQKBTitleCode)TC0000290382(PQKBWorkID)10423115(PQKB)10903229(WSP)00006523(Au-PeEL)EBL1193191(CaPaEBR)ebr10255827(CaONFJC)MIL191905(OCoLC)850162656(Perlego)849039(MiAaPQ)EBC1193191(EXLCZ)99100000000041208120070818d2007 uy 0engurcn|||||||||txtccrBridging the gap between graph edit distance and kernel machines /Michel Neuhaus, Horst Bunke1st ed.Singapore ;Hackensack, NJ World Scientificc20071 online resource (244 p.)Series in machine perception and artificial intelligence ;v. 68Extended and revised version of the first author's PhD thesis.9789812708175 9812708170 Includes bibliographical references (p. 221-230) and index.Preface; Contents; 1. Introduction; 2. Graph Matching; 2.1 Graph and Subgraph; 2.2 Exact Graph Matching; 2.3 Error-Tolerant Graph Matching; 3. Graph Edit Distance; 3.1 Definition; 3.2 Edit Cost Functions; 3.2.1 Conditions on Edit Costs; 3.2.2 Examples of Edit Costs; 3.3 Exact Algorithm; 3.4 Efficient Approximate Algorithm; 3.4.1 Algorithm; 3.4.2 Experimental Results; 3.5 Quadratic Programming Algorithm; 3.5.1 Algorithm; 3.5.1.1 Quadratic Programming; 3.5.1.2 Fuzzy Edit Path; 3.5.1.3 Quadratic Programming Edit Path Optimization; 3.5.2 Experimental Results; 3.6 Nearest-Neighbor Classification3.7 An Application: Data-Level Fusion of Graphs 3.7.1 Fusion of Graphs; 3.7.2 Experimental Results; 4. Kernel Machines; 4.1 Learning Theory; 4.1.1 Empirical Risk Minimization; 4.1.2 Structural Risk Minimization; 4.2 Kernel Functions; 4.2.1 Valid Kernels; 4.2.2 Feature Space Embedding and Kernel Trick; 4.3 Kernel Machines; 4.3.1 Support Vector Machine; 4.3.2 Kernel Principal Component Analysis; 4.3.3 Kernel Fisher Discriminant Analysis; 4.3.4 Using Non-Positive De nite Kernel Functions; 4.4 Nearest-Neighbor Classification Revisited; 5. Graph Kernels; 5.1 Kernel Machines for Graph Matching5.2 Related Work 5.3 Trivial Similarity Kernel from Edit Distance; 5.4 Kernel from Maximum-Similarity Edit Path; 5.5 Diffusion Kernel from Edit Distance; 5.6 Zero Graph Kernel from Edit Distance; 5.7 Convolution Edit Kernel; 5.8 Local Matching Kernel; 5.9 Random Walk Edit Kernel; 6. Experimental Results; 6.1 Line Drawing and Image Graph Data Sets; 6.1.1 Letter Line Drawing Graphs; 6.1.2 Image Graphs; 6.1.3 Diatom Graphs; 6.2 Fingerprint Graph Data Set; 6.2.1 Biometric Person Authentication; 6.2.2 Fingerprint Classification; 6.2.3 Fingerprint Graphs; 6.3 Molecule Graph Data Set6.4 Experimental Setup 6.5 Evaluation of Graph Edit Distance; 6.5.1 Letter Graphs; 6.5.2 Image Graphs; 6.5.3 Diatom Graphs; 6.5.4 Fingerprint Graphs; 6.5.5 Molecule Graphs; 6.6 Evaluation of Graph Kernels; 6.6.1 Trivial Similarity Kernel from Edit Distance; 6.6.2 Kernel from Maximum-Similarity Edit Path; 6.6.3 Diffusion Kernel from Edit Distance; 6.6.4 Zero Graph Kernel from Edit Distance; 6.6.5 Convolution Edit Kernel; 6.6.6 Local Matching Kernel; 6.6.7 Random Walk Edit Kernel; 6.7 Summary and Discussion; 7. Conclusions; Appendix A Graph Data Sets; A.1 Letter Data Set; A.2 Image Data SetA.3 Diatom Data Set A.4 Fingerprint Data Set; A.5 Molecule Data Set; Bibliography; IndexIn graph-based structural pattern recognition, the idea is to transform patterns into graphs and perform the analysis and recognition of patterns in the graph domain - commonly referred to as graph matching. A large number of methods for graph matching have been proposed. Graph edit distance, for instance, defines the dissimilarity of two graphs by the amount of distortion that is needed to transform one graph into the other and is considered one of the most flexible methods for error-tolerant graph matching.This book focuses on graph kernel functions that are highly tolerant towards structuralSeries in machine perception and artificial intelligence ;v. 68.Pattern recognition systemsMatching theoryMachine learningKernel functionsGraph theoryPattern recognition systems.Matching theory.Machine learning.Kernel functions.Graph theory.003.52003/.52006.4Neuhaus Michel1861238Bunke Horst1949-28587MiAaPQMiAaPQMiAaPQBOOK9910953397103321Bridging the gap between graph edit distance and kernel machines4467330UNINA