01689nam 2200517 450 991048055960332120170925021338.01-4704-1895-9(CKB)3710000000387096(SSID)ssj0001331934(PQKBManifestationID)12575309(PQKBTitleCode)TC0001331934(PQKBWorkID)11375486(PQKB)11029263(MiAaPQ)EBC5295325(PPN)195408772(EXLCZ)99371000000038709620180606d2014 uy 0engurcnu||||||||txtccrA power law of order 1/4 for critical mean field Swendsen-Wang dynamics /Yun Long [and three others]Providence, Rhode Island :American Mathematical Society,[2014]©20141 online resource (v, 84 pages)Memoirs of the American Mathematical Society ;Volume 232, Number 1092"Volume 232, number 1092 (fourth of 6 numbers), November 2014."1-4704-0910-0 Includes bibliographical references.Memoirs of the American Mathematical Society ;Volume 232, Number 1092.0065-9266.Markov processesSpin wavesMathematical modelsElectronic books.Markov processes.Spin wavesMathematical models.519.2/33Long Yun1982-MiAaPQMiAaPQMiAaPQBOOK9910480559603321A power law of order 12218880UNINA05364nam 2200769Ia 450 991045155720332120200520144314.01-281-91905-59786611919054981-277-020-8(CKB)1000000000412081(EBL)1193191(SSID)ssj0000290382(PQKBManifestationID)11225459(PQKBTitleCode)TC0000290382(PQKBWorkID)10423115(PQKB)10903229(MiAaPQ)EBC1193191(WSP)00006523(Au-PeEL)EBL1193191(CaPaEBR)ebr10255827(CaONFJC)MIL191905(OCoLC)850162656(EXLCZ)99100000000041208120070818d2007 uy 0engurcn|||||||||txtccrBridging the gap between graph edit distance and kernel machines[electronic resource] /Michel Neuhaus, Horst BunkeSingapore ;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.981-270-817-0 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 theoryElectronic books.Pattern recognition systems.Matching theory.Machine learning.Kernel functions.Graph theory.003.52003/.52006.4Neuhaus Michel911218Bunke Horst28587MiAaPQMiAaPQMiAaPQBOOK9910451557203321Bridging the gap between graph edit distance and kernel machines2040736UNINA01027nam0 22002531i 450 UON0033033620231205104218.4920090908d1966 |0itac50 baengUS|||| |||||Domestic architecture of the American colonies and of the Early RepublicNew YorkDover Publication1966XX, 314 p.ill.27 cm.001UON003303412001 Dover Books on Architecture210 New YorkDover.USNew YorkUONL000050KIMBALLFiskeUONV187987701016DoverUONV246529650ITSOL20240220RICASIBA - SISTEMA BIBLIOTECARIO DI ATENEOUONSIUON00330336SIBA - SISTEMA BIBLIOTECARIO DI ATENEOSI NordA X KIM SI LO 6498 5 Domestic architecture of the American colonies and of the Early Republic1367569UNIOR