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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 | ||
| ||
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 | ||
| ||
Mathematical foundations of big data analytics / / Vladimir Shikhman, David MuÌller
| Mathematical foundations of big data analytics / / Vladimir Shikhman, David MuÌller |
| Autore | Shikhman Vladimir |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Berlin, Germany : , : Springer Gabler, , [2021] |
| Descrizione fisica | 1 online resource (XI, 273 p. 53 illus., 21 illus. in color. Textbook for German language market.) |
| Disciplina | 005.7 |
| Soggetto topico | Big data - Mathematics |
| ISBN | 3-662-62521-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface -- 1 Ranking -- 2 Online Learning -- 3 Recommendation Systems -- 4 Classification -- 5 Clustering -- 6 Linear Regression -- 7 Sparse Recovery -- 8 Neural Networks -- 9 Decision Trees -- 10 Solutions. |
| Record Nr. | UNINA-9910484942203321 |
|
Shikhman Vladimir
|
||
| Berlin, Germany : , : Springer Gabler, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical learning for big dependent data / / Daniel Peña, Ruey S. Tsay
| Statistical learning for big dependent data / / Daniel Peña, Ruey S. Tsay |
| Autore | Peña Daniel <1948-> |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , [2021] |
| Descrizione fisica | 1 online resource (563 pages) |
| Disciplina | 005.7 |
| Collana | Wiley series in probability and statistics |
| Soggetto topico | Big data - Mathematics |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-41741-4
1-119-41740-6 1-119-41739-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910554808803321 |
|
Peña Daniel <1948->
|
||
| Hoboken, New Jersey : , : Wiley, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical learning for big dependent data / / Daniel Peña, Ruey S. Tsay
| Statistical learning for big dependent data / / Daniel Peña, Ruey S. Tsay |
| Autore | Peña Daniel <1948-> |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , [2021] |
| Descrizione fisica | 1 online resource (563 pages) |
| Disciplina | 005.7 |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Big data - Mathematics
Time-series analysis Data mining - Statistical methods Forecasting - Statistical methods |
| ISBN |
1-119-41741-4
1-119-41740-6 1-119-41739-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction to big dependent data -- Linear univariate time series -- Analysis of multivariate time series -- Handling heterogeneity in many time series -- Clustering and classification of time series -- Dynamic factor models -- Forecasting with big dependent data -- Machine learning of big dependent data -- Spatio-temporal dependent data. |
| Record Nr. | UNINA-9910829984503321 |
|
Peña Daniel <1948->
|
||
| Hoboken, New Jersey : , : Wiley, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
