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.