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[null,"1.","Record Nr.",null,"UNINA9910954918203321"]
[null,null,"Autore",null,"Benson Stephen (Stephen Frank)"]
[null,null,"Titolo",null,"Cycles of influence : fiction, folktale, theory / / Stephen Benson"]
[null,null,"Pubbl/distr/stampa",null,"Detroit, : Wayne State University Press, c2003"]
[null,null,"ISBN",null,"9780814339091"]
[{"$nulls":4},"0814339093"]
[null,null,"Edizione",null,"[First edition.]"]
[null,null,"Descrizione fisica",null,"1 online resource (312 pages)"]
[null,null,"Disciplina",null,"809/.9113"]
[null,null,"Soggetti",null,"Literature and folklore"]
[{"$nulls":4},"Postmodernism (Literature)"]
[{"$nulls":4},"Tales - History and criticism"]
[{"$nulls":4},"Fiction - History and criticism"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Cover Title Page Copyright Dedication Contents Acknowledgments Introduction 1. Tales in Theory: The Role of the Folktale in the Development of Narratology 2. Theory in Tales: Cycles, Levels, and Frames 3. The Idea of the Folktale in Italo Calvino Italian Folktales: Text and Contexts First Idea: Tradition and Ideology Second Idea: Singular Fantasies Third Idea: A \"Geometry of Story-Telling\" 4. Narrative Turns John Barth, Author of the Arabian Nights. \"Familiarity Breeds Consent\": Robert Coover and the Fairy Tale 5. Craftiness and Cruelty: A Reading of the Fairy Tale and Its Place in Recent Feminist Fictions \"Curiosity ... Is Insubordination in Its Purest Form\" Conclusion Notes Bibliography Index"]
[null,null,null,"Sommario/riassunto","The arguments presented will interest not only folklorists and scholars of narrative but readers in fields ranging from comparative literature to feminist theory."]
[null,"2.","Record Nr.",null,"UNINA9910970281503321"]
[null,null,"Titolo",null,"Support vector machines : data analysis, machine learning and applications / / Brandon H. Boyle, editor"]
[null,null,"Pubbl/distr/stampa",null,"New York, : Nova Science Publishers, c2011"]
[null,null,"ISBN",null,"1-62257-078-2"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (214 p.)"]
[null,null,"Collana",null,"Computer science, technology, and applications"]
[null,null,"Disciplina",null,"006.4"]
[null,null,"Soggetti",null,"Support vector machines"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"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. "]
[{"$nulls":4},"METHODOLOGY -- 6. RESULTS AND DISCUSSION -- CONCLUSION -- ACKNOWLEDGMENTS -- REFERENCES -- IMAGE INTERPOLATION USING  SUPPORT VECTOR MACHINES -- ABSTRACT."]
[{"$nulls":4},"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."]
[{"$nulls":4},"2.1. Theoretical Model of the Encoder -- 2.2. Theoretical Model of the Decoder -- 2.3. Application of the Theoretical Model for One and Two-Input Encoders -- 2.3.1. One Input Encoder -- 2.3.2. Two Input Encoder -- 3. Support Vector Machine Decoding -- 3.1.1. SVM Decoder Analysis -- 3.1.2. The Training Stage -- 3.1.3. The Decoding Stage -- 3.2. Advantages of SVM Decoder -- 3.3. Complexity of SVM Decoder -- 3.4. SVM Decoder Design -- 3.5. Simulation Results -- 3.5.1 Effect of Training Size on SVM Decoder -- 3.5.2. Effect of Rayleigh's fading -- CONCLUSIONS -- REFERENCES -- PATTERN RECOGNITION FOR MACHINE FAULT DIAGNOSIS USING SUPPORT VECTOR MACHINE -- ABSTRACT -- 1. INTRODUCTION -- 2. PRELIMINARY KNOWLEDGE -- 2.1. Fault Diagnosis -- 2.2. Time Domain Analysis -- 2.3. Frequency Domain Analysis -- 3. FEATURE-BASED DIAGNOSIS SYSTEM -- 3.1. Data Preprocessing -- 3.1.1. Wavelet Transform -- 3.1.2. Averaging -- 3.1.3. Enveloping -- 3.1.4. Cepstrum -- 3.2. Statistical Feature Representation -- 3.2.1. Features in Time Domain -- 3.2.2. Features on Frequency Domain -- 3.2.3. Auto-regression Coefficient -- 3.3. Dimensionality Reduction Using Feature Extraction -- 3.3.1. Principal Component Analysis (PCA) -- 3.3.2. Independent Component Analysis (ICA) -- 3.3.3. Kernel PCA -- 3.3.4. Kernel ICA -- 4. SUPPORT VECTOR MACHINE (SVM) -- 4.1. Basic Theory: Binary Classification by SVM -- 4.2. SVM Solver -- 4.2.1. Quadratic Programming (QP) -- 4.2.2. Sequential Minimum Optimization (SMO) -- 4.3. Multi-class Classification -- 4.3.1. One-Against-All (OAA) -- 4.3.2. One-Against-"]
[{"$nulls":4},"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."]
[{"$nulls":4},"5.5. Results and Discussion -- CONCLUSION -- REFERENCES -- INDEX."]
[null,null,null,"Sommario/riassunto","This book presents topical research in the study of support vector machines. Topics discussed include the support vector machine in medical imaging; monthly air pollution modeling using support vector machine techniques in Spain; support vector machines for image interpolation schemes in image zooming and color array interpolation; using SVM for the prediction of the ultimate capacity of driven piles in cohesionless soils; SVM in medical classification tasks and pattern recognition for machine fault diagnosis using support vector machines."]