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Biblioteca

MARC Lista (tabellare)
Algebras of singular integral operators with kernels controlled by multiple norms / / Alexander Nagel [and three others]
Algebras of singular integral operators with kernels controlled by multiple norms / / Alexander Nagel [and three others]
Autore Nagel Alexander
Pubbl/distr/stampa Providence, RI : , : American Mathematical Society, , [2018]
Descrizione fisica 1 online resource (vii, 141 pages)
Disciplina 515.723
Collana Memoirs of the American Mathematical Society
Soggetto topico Integral operators
Singular integrals
Algebra
Kernel functions
Soggetto genere / forma Electronic books.
ISBN 1-4704-4923-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910480230403321
Nagel Alexander  
Providence, RI : , : American Mathematical Society, , [2018]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Algebras of singular integral operators with kernels controlled by multiple norms / / Alexander Nagel [and three others]
Algebras of singular integral operators with kernels controlled by multiple norms / / Alexander Nagel [and three others]
Autore Nagel Alexander
Pubbl/distr/stampa Providence, RI : , : American Mathematical Society, , [2018]
Descrizione fisica 1 online resource (vii, 141 pages)
Disciplina 515.723
Altri autori (Persone) RicciFulvio <1948->
SteinElias M. <1931->
WaingerStephen <1936->
Collana Memoirs of the American Mathematical Society
Soggetto topico Integral operators
Singular integrals
Algebra
Kernel functions
ISBN 1-4704-4923-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910793326403321
Nagel Alexander  
Providence, RI : , : American Mathematical Society, , [2018]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Algebras of singular integral operators with kernels controlled by multiple norms / / Alexander Nagel [and three others]
Algebras of singular integral operators with kernels controlled by multiple norms / / Alexander Nagel [and three others]
Autore Nagel Alexander
Pubbl/distr/stampa Providence, RI : , : American Mathematical Society, , [2018]
Descrizione fisica 1 online resource (vii, 141 pages)
Disciplina 515.723
Altri autori (Persone) RicciFulvio <1948->
SteinElias M. <1931->
WaingerStephen <1936->
Collana Memoirs of the American Mathematical Society
Soggetto topico Integral operators
Singular integrals
Algebra
Kernel functions
ISBN 1-4704-4923-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910826455903321
Nagel Alexander  
Providence, RI : , : American Mathematical Society, , [2018]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Bridging the gap between graph edit distance and kernel machines [[electronic resource] /] / Michel Neuhaus, Horst Bunke
Bridging the gap between graph edit distance and kernel machines [[electronic resource] /] / Michel Neuhaus, Horst Bunke
Autore Neuhaus Michel
Pubbl/distr/stampa Singapore ; ; Hackensack, NJ, : World Scientific, c2007
Descrizione fisica 1 online resource (244 p.)
Disciplina 003.52
003/.52
006.4
Altri autori (Persone) BunkeHorst
Collana Series in machine perception and artificial intelligence
Soggetto topico Pattern recognition systems
Matching theory
Machine learning
Kernel functions
Graph theory
Soggetto genere / forma Electronic books.
ISBN 1-281-91905-5
9786611919054
981-277-020-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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 Classification
3.7 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 Matching
5.2 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 Set
6.4 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 Set
A.3 Diatom Data Set A.4 Fingerprint Data Set; A.5 Molecule Data Set; Bibliography; Index
Record Nr. UNINA-9910451557203321
Neuhaus Michel  
Singapore ; ; Hackensack, NJ, : World Scientific, c2007
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Bridging the gap between graph edit distance and kernel machines [[electronic resource] /] / Michel Neuhaus, Horst Bunke
Bridging the gap between graph edit distance and kernel machines [[electronic resource] /] / Michel Neuhaus, Horst Bunke
Autore Neuhaus Michel
Pubbl/distr/stampa Singapore ; ; Hackensack, NJ, : World Scientific, c2007
Descrizione fisica 1 online resource (244 p.)
Disciplina 003.52
003/.52
006.4
Altri autori (Persone) BunkeHorst
Collana Series in machine perception and artificial intelligence
Soggetto topico Pattern recognition systems
Matching theory
Machine learning
Kernel functions
Graph theory
ISBN 1-281-91905-5
9786611919054
981-277-020-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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 Classification
3.7 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 Matching
5.2 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 Set
6.4 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 Set
A.3 Diatom Data Set A.4 Fingerprint Data Set; A.5 Molecule Data Set; Bibliography; Index
Record Nr. UNINA-9910777303003321
Neuhaus Michel  
Singapore ; ; Hackensack, NJ, : World Scientific, c2007
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Bridging the gap between graph edit distance and kernel machines [[electronic resource] /] / Michel Neuhaus, Horst Bunke
Bridging the gap between graph edit distance and kernel machines [[electronic resource] /] / Michel Neuhaus, Horst Bunke
Autore Neuhaus Michel
Pubbl/distr/stampa Singapore ; ; Hackensack, NJ, : World Scientific, c2007
Descrizione fisica 1 online resource (244 p.)
Disciplina 003.52
003/.52
006.4
Altri autori (Persone) BunkeHorst
Collana Series in machine perception and artificial intelligence
Soggetto topico Pattern recognition systems
Matching theory
Machine learning
Kernel functions
Graph theory
ISBN 1-281-91905-5
9786611919054
981-277-020-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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 Classification
3.7 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 Matching
5.2 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 Set
6.4 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 Set
A.3 Diatom Data Set A.4 Fingerprint Data Set; A.5 Molecule Data Set; Bibliography; Index
Record Nr. UNINA-9910807377703321
Neuhaus Michel  
Singapore ; ; Hackensack, NJ, : World Scientific, c2007
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data-variant kernel analysis / / Yuichi Motai
Data-variant kernel analysis / / Yuichi Motai
Autore Motai Yuichi
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , 2015
Descrizione fisica 1 online resource (248 p.)
Disciplina 515/.9
Collana Wiley Series on Adaptive and Cognitive Dynamic Systems
Soggetto topico Kernel functions
Big data - Mathematics
ISBN 1-119-01934-6
1-119-01935-4
Classificazione COM051300
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title Page; Copyright; Contents; List of Figures; List of Tables; Preface; Acknowledgments; Chapter 1 Survey; 1.1 Introduction of Kernel Analysis; 1.2 Kernel Offline Learning; 1.2.1 Choose the Appropriate Kernels; 1.2.2 Adopt KA into the Traditionally Developed Machine Learning Techniques; 1.2.3 Structured Database with Kernel; 1.3 Distributed Database with Kernel; 1.3.1 Multiple Database Representation; 1.3.2 Kernel Selections Among Heterogeneous Multiple Databases; 1.3.3 Multiple Database Representation KA Applications to Distributed Databases; 1.4 Kernel Online Learning
1.4.1 Kernel-Based Online Learning Algorithms1.4.2 Adopt ""Online"" KA Framework into the Traditionally Developed Machine Learning Techniques; 1.4.3 Relationship Between Online Learning and Prediction Techniques; 1.5 Prediction with Kernels; 1.5.1 Linear Prediction; 1.5.2 Kalman Filter; 1.5.3 Finite-State Model; 1.5.4 Autoregressive Moving Average Model; 1.5.5 Comparison of Four Models; 1.6 Future Direction and Conclusion; References; Chapter 2 Offline Kernel Analysis; 2.1 Introduction; 2.2 Kernel Feature Analysis; 2.2.1 Kernel Basics; 2.2.2 Kernel Principal Component Analysis (KPCA)
2.2.3 Accelerated Kernel Feature Analysis (AKFA)2.2.4 Comparison of the Relevant Kernel Methods; 2.3 Principal Composite Kernel Feature Analysis (PC-KFA); 2.3.1 Kernel Selections; 2.3.2 Kernel Combinatory Optimization; 2.4 Experimental Analysis; 2.4.1 Cancer Image Datasets; 2.4.2 Kernel Selection; 2.4.3 Kernel Combination and Reconstruction; 2.4.4 Kernel Combination and Classification; 2.4.5 Comparisons of Other Composite Kernel Learning Studies; 2.4.6 Computation Time; 2.5 Conclusion; References; Chapter 3 Group Kernel Feature Analysis; 3.1 Introduction
3.2 Kernel Principal Component Analysis (KPCA)3.3 Kernel Feature Analysis (KFA) for Distributed Databases; 3.3.1 Extract Data-Dependent Kernels Using KFA; 3.3.2 Decomposition of Database Through Data Association via Recursively Updating Kernel Matrices; 3.4 Group Kernel Feature Analysis (GKFA); 3.4.1 Composite Kernel: Kernel Combinatory Optimization; 3.4.2 Multiple Databases Using Composite Kernel; 3.5 Experimental Results; 3.5.1 Cancer Databases; 3.5.2 Optimal Selection of Data-Dependent Kernels; 3.5.3 Kernel Combinatory Optimization; 3.5.4 Composite Kernel for Multiple Databases
3.5.5 K-NN Classification Evaluation with ROC3.5.6 Comparison of Results with Other Studies on Colonography; 3.5.7 Computational Speed and Scalability Evaluation of GKFA; 3.6 Conclusions; References; Chapter 4 Online Kernel Analysis; 4.1 Introduction; 4.2 Kernel Basics: A Brief Review; 4.2.1 Kernel Principal Component Analysis; 4.2.2 Kernel Selection; 4.3 Kernel Adaptation Analysis of PC-KFA; 4.4 Heterogeneous vs. Homogeneous Data for Online PC-KFA; 4.4.1 Updating the Gram Matrix of the Online Data; 4.4.2 Composite Kernel for Online Data
4.5 Long-Term Sequential Trajectories with Self-Monitoring
Record Nr. UNINA-9910140625603321
Motai Yuichi  
Hoboken, New Jersey : , : Wiley, , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data-variant kernel analysis / / Yuichi Motai
Data-variant kernel analysis / / Yuichi Motai
Autore Motai Yuichi
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , 2015
Descrizione fisica 1 online resource (248 p.)
Disciplina 515/.9
Collana Wiley Series on Adaptive and Cognitive Dynamic Systems
Soggetto topico Kernel functions
Big data - Mathematics
ISBN 1-119-01934-6
1-119-01935-4
Classificazione COM051300
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title Page; Copyright; Contents; List of Figures; List of Tables; Preface; Acknowledgments; Chapter 1 Survey; 1.1 Introduction of Kernel Analysis; 1.2 Kernel Offline Learning; 1.2.1 Choose the Appropriate Kernels; 1.2.2 Adopt KA into the Traditionally Developed Machine Learning Techniques; 1.2.3 Structured Database with Kernel; 1.3 Distributed Database with Kernel; 1.3.1 Multiple Database Representation; 1.3.2 Kernel Selections Among Heterogeneous Multiple Databases; 1.3.3 Multiple Database Representation KA Applications to Distributed Databases; 1.4 Kernel Online Learning
1.4.1 Kernel-Based Online Learning Algorithms1.4.2 Adopt ""Online"" KA Framework into the Traditionally Developed Machine Learning Techniques; 1.4.3 Relationship Between Online Learning and Prediction Techniques; 1.5 Prediction with Kernels; 1.5.1 Linear Prediction; 1.5.2 Kalman Filter; 1.5.3 Finite-State Model; 1.5.4 Autoregressive Moving Average Model; 1.5.5 Comparison of Four Models; 1.6 Future Direction and Conclusion; References; Chapter 2 Offline Kernel Analysis; 2.1 Introduction; 2.2 Kernel Feature Analysis; 2.2.1 Kernel Basics; 2.2.2 Kernel Principal Component Analysis (KPCA)
2.2.3 Accelerated Kernel Feature Analysis (AKFA)2.2.4 Comparison of the Relevant Kernel Methods; 2.3 Principal Composite Kernel Feature Analysis (PC-KFA); 2.3.1 Kernel Selections; 2.3.2 Kernel Combinatory Optimization; 2.4 Experimental Analysis; 2.4.1 Cancer Image Datasets; 2.4.2 Kernel Selection; 2.4.3 Kernel Combination and Reconstruction; 2.4.4 Kernel Combination and Classification; 2.4.5 Comparisons of Other Composite Kernel Learning Studies; 2.4.6 Computation Time; 2.5 Conclusion; References; Chapter 3 Group Kernel Feature Analysis; 3.1 Introduction
3.2 Kernel Principal Component Analysis (KPCA)3.3 Kernel Feature Analysis (KFA) for Distributed Databases; 3.3.1 Extract Data-Dependent Kernels Using KFA; 3.3.2 Decomposition of Database Through Data Association via Recursively Updating Kernel Matrices; 3.4 Group Kernel Feature Analysis (GKFA); 3.4.1 Composite Kernel: Kernel Combinatory Optimization; 3.4.2 Multiple Databases Using Composite Kernel; 3.5 Experimental Results; 3.5.1 Cancer Databases; 3.5.2 Optimal Selection of Data-Dependent Kernels; 3.5.3 Kernel Combinatory Optimization; 3.5.4 Composite Kernel for Multiple Databases
3.5.5 K-NN Classification Evaluation with ROC3.5.6 Comparison of Results with Other Studies on Colonography; 3.5.7 Computational Speed and Scalability Evaluation of GKFA; 3.6 Conclusions; References; Chapter 4 Online Kernel Analysis; 4.1 Introduction; 4.2 Kernel Basics: A Brief Review; 4.2.1 Kernel Principal Component Analysis; 4.2.2 Kernel Selection; 4.3 Kernel Adaptation Analysis of PC-KFA; 4.4 Heterogeneous vs. Homogeneous Data for Online PC-KFA; 4.4.1 Updating the Gram Matrix of the Online Data; 4.4.2 Composite Kernel for Online Data
4.5 Long-Term Sequential Trajectories with Self-Monitoring
Record Nr. UNINA-9910812500603321
Motai Yuichi  
Hoboken, New Jersey : , : Wiley, , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Differential geometry : proc. of the Nordic Summer School held in Lyngby, Denmark July 29-August 9, 1985 / ed. by V. L. Hansen
Differential geometry : proc. of the Nordic Summer School held in Lyngby, Denmark July 29-August 9, 1985 / ed. by V. L. Hansen
Autore Hansen, Vagn Lundsgaard
Pubbl/distr/stampa Berlin ; Heidelberg : Springer-Verlag, 1987
Descrizione fisica 150 p. ; 24 cm.
Disciplina 516.36
Collana Lecture notes in mathematics, 0075-8434 ; 1263
Soggetto topico Complex manifolds
Differential geometry - Congresses
Holomorphic mappings
Kernel functions
ISBN 3540180125
Classificazione AMS 32C10
AMS 32H10
AMS 53-06
AMS 53-XX
AMS 53C05
AMS 53C20
AMS 53C55
AMS 53C80
AMS 58E05
AMS 58E20
AMS 58G30
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISALENTO-991000820499707536
Hansen, Vagn Lundsgaard  
Berlin ; Heidelberg : Springer-Verlag, 1987
Materiale a stampa
Lo trovi qui: Univ. del Salento
Opac: Controlla la disponibilità qui
General irreducible Markov chains and non-negative operators / Esa Nummelin
General irreducible Markov chains and non-negative operators / Esa Nummelin
Autore Nummelin, Esa
Pubbl/distr/stampa Cambridge ; New York : Cambridge University Press, 1984
Descrizione fisica xi, 156 p. ; 24 cm.
Disciplina 519.233
Collana Cambridge tracts in mathematics ; 83
Soggetto topico Kernel functions
Markov processes
Operator theory
ISBN 0521250056
Classificazione AMS 60-XX
AMS 60J
QA274.7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione en
Record Nr. UNISALENTO-991000928379707536
Nummelin, Esa  
Cambridge ; New York : Cambridge University Press, 1984
Materiale a stampa
Lo trovi qui: Univ. del Salento
Opac: Controlla la disponibilità qui