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Generalized Mercer kernels and reproducing kernel Banach spaces / / Yuesheng Xu, Qi Ye
| Generalized Mercer kernels and reproducing kernel Banach spaces / / Yuesheng Xu, Qi Ye |
| Autore | Xu Yuesheng |
| Pubbl/distr/stampa | Providence, RI : , : American Mathematical Society, , [2019] |
| Descrizione fisica | 1 online resource (134 pages) |
| Disciplina | 515/.732 |
| Collana | Memoirs of the American Mathematical Society |
| Soggetto topico |
Kernel functions
Geometric function theory Banach spaces Functions of complex variables Support vector machines |
| Soggetto genere / forma | Electronic books. |
| ISBN | 1-4704-5077-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910480159203321 |
Xu Yuesheng
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| Providence, RI : , : American Mathematical Society, , [2019] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Generalized Mercer kernels and reproducing kernel Banach spaces / / Yuesheng Xu, Qi Ye
| Generalized Mercer kernels and reproducing kernel Banach spaces / / Yuesheng Xu, Qi Ye |
| Autore | Xu Yuesheng |
| Pubbl/distr/stampa | Providence, RI : , : American Mathematical Society, , [2019] |
| Descrizione fisica | 1 online resource (134 pages) |
| Disciplina | 515/.732 |
| Collana | Memoirs of the American Mathematical Society |
| Soggetto topico |
Kernel functions
Geometric function theory Banach spaces Functions of complex variables Support vector machines |
| ISBN | 1-4704-5077-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910793424703321 |
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Xu Yuesheng
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| Providence, RI : , : American Mathematical Society, , [2019] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Generalized Mercer kernels and reproducing kernel Banach spaces / / Yuesheng Xu, Qi Ye
| Generalized Mercer kernels and reproducing kernel Banach spaces / / Yuesheng Xu, Qi Ye |
| Autore | Xu Yuesheng |
| Pubbl/distr/stampa | Providence, RI : , : American Mathematical Society, , [2019] |
| Descrizione fisica | 1 online resource (134 pages) |
| Disciplina | 515/.732 |
| Collana | Memoirs of the American Mathematical Society |
| Soggetto topico |
Kernel functions
Geometric function theory Banach spaces Functions of complex variables Support vector machines |
| ISBN | 1-4704-5077-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910812390203321 |
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Xu Yuesheng
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| Providence, RI : , : American Mathematical Society, , [2019] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Knowledge discovery with support vector machines / / Lutz Hamel
| Knowledge discovery with support vector machines / / Lutz Hamel |
| Autore | Hamel Lutz |
| Pubbl/distr/stampa | Hoboken, NJ, : John Wiley & Sons, 2009 |
| Descrizione fisica | 1 online resource (266 p.) |
| Disciplina | 005.1 |
| Collana | Wiley series on methods and applications in data mining |
| Soggetto topico |
Support vector machines
Data mining Machine learning Computer algorithms |
| ISBN |
9786612345661
9781118211038 1118211030 9781282345669 1282345664 9780470503065 0470503068 9780470503041 0470503041 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
KNOWLEDGE DISCOVERY WITH SUPPORT VECTOR MACHINES; CONTENTS; PREFACE; PART I; 1 WHAT IS KNOWLEDGE DISCOVERY?; 2 KNOWLEDGE DISCOVERY ENVIRONMENTS; 3 DESCRIBING DATA MATHEMATICALLY; 4 LINEAR DECISION SURFACES AND FUNCTIONS; 5 PERCEPTRON LEARNING; 6 MAXIMUM-MARGIN CLASSIFIERS; PART II; 7 SUPPORT VECTOR MACHINES; 8 IMPLEMENTATION; 9 EVALUATING WHAT HAS BEEN LEARNED; 10 ELEMENTS OF STATISTICAL LEARNING THEORY; PART III; 11 MULTICLASS CLASSIFICATION; 12 REGRESSION WITH SUPPORT VECTOR MACHINES; 13 NOVELTY DETECTION; APPENDIX A NOTATION; APPENDIX B TUTORIAL INTRODUCTION TO R
B.1 Programming ConstructsB.2 Data Constructs; B.3 Basic Data Analysis; Bibliographic Notes; REFERENCES; INDEX |
| Record Nr. | UNINA-9910139896403321 |
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Hamel Lutz
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| Hoboken, NJ, : John Wiley & Sons, 2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Proceedings of the 2018 International Conference on Signal Processing and Machine Learning / / Association for Computing Machinery
| Proceedings of the 2018 International Conference on Signal Processing and Machine Learning / / Association for Computing Machinery |
| Pubbl/distr/stampa | New York, NY, United States : , : Association for Computing Machinery, , 2018 |
| Descrizione fisica | 1 online resource (177 pages) |
| Disciplina | 006.31 |
| Soggetto topico |
Machine learning
Support vector machines |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910375774903321 |
| New York, NY, United States : , : Association for Computing Machinery, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining / / Aidong Zhang, editor
| Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining / / Aidong Zhang, editor |
| Pubbl/distr/stampa | New York, NY, United States : , : Association for Computing Machinery, , 2022 |
| Descrizione fisica | 1 online resource (5033 pages) |
| Disciplina | 006.31 |
| Collana | ACM Conferences |
| Soggetto topico |
Machine learning
Support vector machines |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | KDD '22 |
| Record Nr. | UNINA-9910588792503321 |
| New York, NY, United States : , : Association for Computing Machinery, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Support vector machines [[electronic resource] ] : data analysis, machine learning and applications / / Brandon H. Boyle, editor
| Support vector machines [[electronic resource] ] : data analysis, machine learning and applications / / Brandon H. Boyle, editor |
| Pubbl/distr/stampa | New York, : Nova Science Publishers, c2011 |
| Descrizione fisica | 1 online resource (214 p.) |
| Disciplina | 006.4 |
| Altri autori (Persone) | BoyleBrandon H |
| Collana | Computer science, technology, and applications |
| Soggetto topico | Support vector machines |
| Soggetto genere / forma | Electronic books. |
| ISBN | 1-62257-078-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910453819603321 |
| New York, : Nova Science Publishers, c2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Support vector machines [[electronic resource] ] : data analysis, machine learning and applications / / Brandon H. Boyle, editor
| Support vector machines [[electronic resource] ] : data analysis, machine learning and applications / / Brandon H. Boyle, editor |
| Pubbl/distr/stampa | New York, : Nova Science Publishers, c2011 |
| Descrizione fisica | 1 online resource (214 p.) |
| Disciplina | 006.4 |
| Altri autori (Persone) | BoyleBrandon H |
| Collana | Computer science, technology, and applications |
| Soggetto topico | Support vector machines |
| ISBN | 1-62257-078-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910779791603321 |
| New York, : Nova Science Publishers, c2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Support vector machines : data analysis, machine learning and applications / / Brandon H. Boyle, editor
| Support vector machines : data analysis, machine learning and applications / / Brandon H. Boyle, editor |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | New York, : Nova Science Publishers, c2011 |
| Descrizione fisica | 1 online resource (214 p.) |
| Disciplina | 006.4 |
| Altri autori (Persone) | BoyleBrandon H |
| Collana | Computer science, technology, and applications |
| Soggetto topico | Support vector machines |
| ISBN | 1-62257-078-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- SUPPORT VECTOR MACHINES: DATA ANALYSIS, MACHINE LEARNING AND APPLICATIONS -- SUPPORT VECTOR MACHINES: DATA ANALYSIS, MACHINE LEARNING AND APPLICATIONS -- CONTENTS -- PREFACE -- THE SUPPORT VECTOR MACHINE IN MEDICAL IMAGING -- ABSTRACT -- 1. INTRODUCTION -- 2. THE SUPPORT VECTOR MACHINE -- 3. THE SUPPORT VECTOR MACHINE'S USE IN MEDICAL IMAGING -- 3.1. Breast Cancer Imaging -- 3.2. Brain Imaging -- 3.3. Skin and Oral Imaging -- 3.4. Liver Imaging -- 3.5. Lung Imaging -- 3.6. Reproductive System Imaging -- 3.7. Eye Imaging -- 3.8. Other Imaging Applications -- 4. CASE STUDY: THE SUPPORT VECTOR MACHINE IN BREAST CANCER DETECTION FROM MAGNETIC RESONANCE IMAGING -- 4.1. Case Study - Introduction -- 4.2. Case Study - Methods -- Support Vector Machine Classification -- Proposed Vector Machine Formulations -- Breast MRI Database for Case Study -- Image Acquisition and Data Preprocessing -- Breast MR Lesion Measurements -- Feature Measurement #1: Average Slope -- Feature Measurement #2: Average Washout -- Feature Measurement #3: Sphericity / Irregularity -- Feature Measurement #4: Average Edge Diffuseness -- Receiver Operating Characteristic Curve Analysis and Validation -- 4.3. Case Study - Results -- 4.4. Case Study - Discussion -- 4.5. Case Study - Conclusions -- CONCLUSION -- ACKNOWLEDGMENTS -- REFERENCES -- A SVM-BASED REGRESSION MODEL TO STUDY THE AIR QUALITY IN THE URBAN AREA OF THE CITY OF OVIEDO (SPAIN) -- ABSTRACT -- 1. INTRODUCTION -- 2. SOURCES AND TYPES OF AIR POLLUTION -- 2.1. Primary Pollutants -- 2.2. Secondary Pollutants -- 2.3. Trends in Air Quality -- 3. MATHEMATICAL MODEL -- 3.1. Non-Linear Support Vector Machines -- 4. EXPERIMENTAL DATA SET -- 5. METHODOLOGY -- 6. RESULTS AND DISCUSSION -- CONCLUSION -- ACKNOWLEDGMENTS -- REFERENCES -- IMAGE INTERPOLATION USING SUPPORT VECTOR MACHINES -- ABSTRACT.
1. INTRODUCTION OF IMAGE INTERPOLATION -- 1.1. Linear and Cubic Image Interpolation -- 1.2. Support Vector Regression -- 2. SUPPORT VECTOR MACHINES BASED IMAGE INTERPOLATION -- 2.1. Data Fitting Image Interpolation Approach -- 2.2. Neighbor Pixel Image Interpolation Approach -- 2.3. Local Spatial Properties Image Interpolation Approach -- 2.4. Conclusion -- 3. SUPPORT VECTOR MACHINES BASED INTERPOLATION FOR COLOR FILTER ARRAY -- 3.1. Introduction to Color Filter Array Interpolation -- 3.2 Color Filter Array Interpolation Using SVR -- 3.3. Experiments -- ACKNOWLEDGMENT -- REFERENCES -- UTILIZATION OF SUPPORT VECTOR MACHINE (SVM) FOR PREDICTION OF ULTIMATE CAPACITY OF DRIVEN PILES IN COHESIONLESS SOILS -- ABSTRACT: -- INTRODUCTION -- DETAILS OF SVM MODEL -- RESULTS AND DISCUSSION -- CONCLUSION -- REFERENCES -- SUPPORT VECTOR MACHINES IN MEDICAL CLASSIFICATION TASKS -- 1.Introduction -- 2.SupportVectorMachines -- 3.Experimentation -- 3.1.BreastCancerDatabase -- 3.2.ParkinsonDatabase -- 3.3.UrologicalDatabase -- 3.3.1.DimensionalityReduction -- 3.3.2.ArchitectureoftheSVM -- 4.Conclusions -- Acknowledgment -- References -- KERNEL LATENT SEMANTIC ANALYSIS USING TERM FUSION KERNELS -- Abstract -- 1.Introduction -- 2.KernelCombinationforTextMiningTasks -- 3.Application:LatentSemanticClassExtractioninTextMining -- 3.1.Assigningprobabilitiesoftermstosemanticclasses -- 4.Experimentalwork -- 5.Conclusions -- Acknowledgments -- References -- SVR FOR TIME SERIES PREDICTION -- Abstract -- 1. INTRODUCTION -- 2. RELATED WORK -- 3. PREDICTION MODELS -- 3.1 Artificial Neural Networks -- 3.2 Support Vector Machines -- 3.3 Support Vector Predictors (SVP) -- 4. EXPERIMENTS -- 5. CONCLUSION -- REFERENCES -- APPLICATION OF NEURAL NETWORKS AND SUPPORT VECTOR MACHINES IN CODING THEORY AND PRACTICE -- Abstract -- 1. INTRODUCTION -- 2. RECURRENT NEURAL NETWORK DECODING. 2.1. Theoretical Model of the Encoder -- 2.2. Theoretical Model of the Decoder -- 2.3. Application of the Theoretical Model for One and Two-Input Encoders -- 2.3.1. One Input Encoder -- 2.3.2. Two Input Encoder -- 3. Support Vector Machine Decoding -- 3.1.1. SVM Decoder Analysis -- 3.1.2. The Training Stage -- 3.1.3. The Decoding Stage -- 3.2. Advantages of SVM Decoder -- 3.3. Complexity of SVM Decoder -- 3.4. SVM Decoder Design -- 3.5. Simulation Results -- 3.5.1 Effect of Training Size on SVM Decoder -- 3.5.2. Effect of Rayleigh's fading -- CONCLUSIONS -- REFERENCES -- PATTERN RECOGNITION FOR MACHINE FAULT DIAGNOSIS USING SUPPORT VECTOR MACHINE -- ABSTRACT -- 1. INTRODUCTION -- 2. PRELIMINARY KNOWLEDGE -- 2.1. Fault Diagnosis -- 2.2. Time Domain Analysis -- 2.3. Frequency Domain Analysis -- 3. FEATURE-BASED DIAGNOSIS SYSTEM -- 3.1. Data Preprocessing -- 3.1.1. Wavelet Transform -- 3.1.2. Averaging -- 3.1.3. Enveloping -- 3.1.4. Cepstrum -- 3.2. Statistical Feature Representation -- 3.2.1. Features in Time Domain -- 3.2.2. Features on Frequency Domain -- 3.2.3. Auto-regression Coefficient -- 3.3. Dimensionality Reduction Using Feature Extraction -- 3.3.1. Principal Component Analysis (PCA) -- 3.3.2. Independent Component Analysis (ICA) -- 3.3.3. Kernel PCA -- 3.3.4. Kernel ICA -- 4. SUPPORT VECTOR MACHINE (SVM) -- 4.1. Basic Theory: Binary Classification by SVM -- 4.2. SVM Solver -- 4.2.1. Quadratic Programming (QP) -- 4.2.2. Sequential Minimum Optimization (SMO) -- 4.3. Multi-class Classification -- 4.3.1. One-Against-All (OAA) -- 4.3.2. One-Against-One (OAO) -- 4.3.3. Direct Acyclic Graph (DAG) -- 4.4. Wavelet-Support Vector Machine (W-SVM) -- 5. APPLICATION FOR FAULT DIAGNOSIS OF INDUCTION MOTOR -- 5.1. Fault Diagnosis Method -- 5.2. Experiment and Data Acquisition -- 5.3. Feature Extraction and Reduction -- 5.4. Classification. 5.5. Results and Discussion -- CONCLUSION -- REFERENCES -- INDEX. |
| Record Nr. | UNINA-9910970281503321 |
| New York, : Nova Science Publishers, c2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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