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• Introduction to SVM
• Basics of SVM Method and Least Squares SVM
• Fractional Chebyshev Kernel Functions: Theory and Application
• Fractional Legendre Kernel Functions: Theory and Application
• Fractional Gegenbauer Kernel Functions: Theory and Application
• Fractional Jacobi Kernel Functions: Theory and Application
• Solving Ordinary Differential Equations by LS-SVM
• Solving Partial Differential Equations by LS-SVM
• Solving Integral Equations by LS-SVR
• Solving Distributed-Order Fractional Equations by LS-SVR
• GPU Acceleration of LS-SVM, Based on Fractional Orthogonal Functions
• Classification Using Orthogonal Kernel Functions: Tutorial on ORSVM Package.

This book contains select chapters on support vector algorithms from different perspectives, including mathematical background, properties of various kernel functions, and several applications. The main focus of this book is on orthogonal kernel functions, and the properties of the classical kernel functionsChebyshev, Legendre, Gegenbauer, and Jacobiare reviewed in some chapters. Moreover, the fractional form of these kernel functions is introduced in the same chapters, and for ease of use for these kernel functions, a tutorial on a Python package named ORSVM is presented. The book also exhibits a variety of applications for support vector algorithms, and in addition to the classification, these algorithms along with the introduced kernel functions are utilized for solving ordinary, partial, integro, and fractional differential equations. On the other hand, nowadays, the real-time and big data applications of support vector algorithms are growing. Consequently, the Compute Unified Device Architecture (CUDA) parallelizing the procedure of support vector algorithms based on orthogonal kernel functions is presented. The book sheds light on how to use support vector algorithms based on orthogonal kernel functions in different situations and gives a significant perspective to all machine learning and scientific machine learning researchers all around the world to utilize fractional orthogonal kernel functions in their pattern recognition or scientific computing problems.

• Overview of support vector machines Background Maximal Interval Linear Classifier Kernel Functions and Kernel Matrix Optimization Theory Elements of Support Vector Machines Applications of Support Vector Machines Support vector machines for classification and regression Kernel Functions and Dimension Superiority Notion of Kernel Functions Kernel Matrix Support Vector Machines for Classification Computing SVMs for Linearly Separable Case Computing SVMs for Linearly Inseparable Case Application of SVC to Simulated Data Support Vector Machines for Regression epsilon-Band and epsilon-Insensitive Loss Function Linear epsilon-SVR Kernel-Based epsilon-SVR Application of SVR to Simulated Data Parametric Optimization for Support Vector Machines Variable Selection for Support Vector Machines Related Materials and Comments VC Dimension Kernel Functions and Quadratic Programming Dimension Increasing versus Dimension Reducing Appendix A: Computation of Slack Variable-Based SVMs Appendix B: Computation of Linear epsilon-SVR Kernel methods Kernel Methods: Three Key Ingredients Primal and Dual Forms Nonlinear Mapping Kernel Function and Kernel Matrix Modularity of Kernel Methods Kernel Principal Component Analysis Kernel Partial Least Squares Kernel Fisher Discriminant Analysis Relationship between Kernel Function and SVMs Kernel Matrix Pretreatment Internet Resources Ensemble learning of support vector machines Ensemble Learning Idea of Ensemble Learning Diversity of Ensemble Learning Bagging Support Vector Machines Boosting Support Vector Machines Boosting: A Simple Example Boosting SVMs for Classification Boosting SVMs for Regression Further Consideration Support vector machines applied to near-infrared spectroscopy Near-Infrared Spectroscopy Support Vector Machines for Classification of Near-Infrared Data Recognition of Blended Vinegar Based on Near-Infrared Spectroscopy Related Work on Support Vector Classification on NIR Support Vector Machines for Quantitative Analysis of Near-Infrared Data Correlating Diesel Boiling Points with NIR Spectra Using SVR Related Work on Support Vector Regression on NIR Some Comments Support vector machines and QSAR/QSPR Quantitative Structure-Activity/Property Relationship History of QSAR/QSPR and Molecular Descriptors Principles for QSAR Modeling Related QSAR/QSPR Studies Using SVMs Support Vector Machines for Regression Dataset Description Molecular Modeling and Descriptor Calculation Feature Selection Using a Generalized Cross-Validation Program Model Internal Validation PLS Regression Model BPN Regression Model SVR Model Applicability Domain and External Validation Model Interpretation Support Vector Machines for Classification Two-Step Algorithm: KPCA Plus LSVM Dataset Description Performance Evaluation Effects of Model Parameters Prediction Results for Three SAR Datasets Support vector machines applied to traditional Chinese medicine Introduction Traditional Chinese Medicines and Their Quality Control Recognition of Authentic PCR and PCRV Using SVM Background Data Description Recognition of Authentic PCR and PCRV Using Whole Chromatography Variable Selection Improves Performance of SVM Some Remarks Support vector machines applied to OMICS study A Brief Description of OMICS Study Support Vector Machines in Genomics Support Vector Machines for Identifying Proteotypic Peptides in Proteomics Biomarker Discovery in Metabolomics Using Support Vector Machines Some Remarks Index.
• (source: Nielsen Book Data)

Support vector machines (SVMs) are used in a range of applications, including drug design, food quality control, metabolic fingerprint analysis, and microarray data-based cancer classification. While most mathematicians are well-versed in the distinctive features and empirical performance of SVMs, many chemists and biologists are not as familiar with what they are and how they work. Presenting a clear bridge between theory and application, Support Vector Machines and Their Application in Chemistry and Biotechnology provides a thorough description of the mechanism of SVMs from the point of view of chemists and biologists, enabling them to solve difficult problems with the help of these powerful tools. Topics discussed include: Background and key elements of support vector machines and applications in chemistry and biotechnology Elements and algorithms of support vector classification (SVC) and support vector regression (SVR) machines, along with discussion of simulated datasets The kernel function for solving nonlinear problems by using a simple linear transformation method Ensemble learning of support vector machines Applications of support vector machines to near-infrared data Support vector machines and quantitative structure-activity/property relationship (QSAR/QSPR) Quality control of traditional Chinese medicine by means of the chromatography fingerprint technique The use of support vector machines in exploring the biological data produced in OMICS study Beneficial for chemical data analysis and the modeling of complex physic-chemical and biological systems, support vector machines show promise in a myriad of areas. This book enables non-mathematicians to understand the potential of SVMs and utilize them in a host of applications.
(source: Nielsen Book Data)

• Preface
• The Support Vector Machine in Medical Imaging
• A SVM-Based Regression Model to Study the Air Quality in the Urban Area of the City of Oviedo (Spain)
• Image Interpolation Using Support Vector Machines
• Utilization of Support Vector Machine (SVM) for Prediction of Ultimate Capacity of Driven Piles in Cohesionless Soils
• Support Vector Machines in Medical Classification Tasks
• Solving Text Mining Problems using Support Vector Machines with Complex Data Oriented Kernels
• Subspace-Based Support Vector Machines
• SVR for Time Series Prediction
• Application of Neural Networks & Support Vector Machines in Coding Theory & Practice
• Pattern Recognition for Machine Fault Diagnosis using Support Vector Machines
• Index.
• (source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

• Augmented-SVM for gradient observations with application to learning multiple-attractor dynamics.- Multi-class Support Vector Machine.- Novel Inductive and Transductive Transfer Learning Approaches Based on Support Vector Learning.- Security Evaluation of Support Vector Machines in Adversarial Environments.- Application of SVMs to the Bag-of-features Model- A Kernel Perspective.- Support Vector Machines for Neuroimage Analysis: Interpretation from Discrimination.- Kernel Machines for Imbalanced Data Problem and the Use in Biomedical Applications.- Soft Biometrics from Face Images using Support Vector Machines.
• (source: Nielsen Book Data)

Support vector machines (SVM) have both a solid mathematical background and practical applications. This book focuses on the recent advances and applications of the SVM, such as image processing, medical practice, computer vision, and pattern recognition, machine learning, applied statistics, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications.
(source: Nielsen Book Data)

• Introduction to support vector machines / Pooja Saigal, PhD, Vivekananda School of Information Technology, Vivekananda Institute of Professional Studies, New Delhi, India
• Journey of support vector machines : from maximum-margin hyperplane to a pair of non-parallel hyperplanes / Pooja Saigal, PhD, Vivekananda School of Information Technology, Vivekananda Institute of Professional Studies, New Delhi, India
• Power spectrum entropy-based support vector machine for quantitative diagnosis of rotor vibration process faults / Cheng-Wei Fei, Department of Aeronautics and Astronautics, Fudan University, Shanghai, China.

"Support Vector Machines: Evolution and Applications reviews the basics of Support Vector Machines (SVM), their evolution and applications in diverse fields. SVM is an efficient supervised learning approach popularly used for pattern recognition, medical image classification, face recognition and various other applications. In the last 25 years, a lot of research has been carried out to extend the use of SVM to a variety of domains. This book is an attempt to present the description of a conventional SVM, along with discussion of its different versions and recent application areas. The first chapter of this book introduces SVM and presents the optimization problems for a conventional SVM. Another chapter discusses the journey of SVM over a period of more than two decades. SVM is proposed as a separating hyperplane classifier that partitions the data belonging to two classes. Later on, various versions of SVM are proposed that obtain two hyperplanes instead of one. A few of these variants of SVM are discussed in this book. The major part of this book discusses some interesting applications of SVM in areas like quantitative diagnosis of rotor vibration process faults through power spectrum entropy-based SVM, hardware architectures of SVM applied in pattern recognition systems, speaker recognition using SVM, classification of iron ore in mines and simultaneous prediction of the density and viscosity for the ternary system water- ethanol-ethylene glycol ionic liquids. The latter part of the book is dedicated to various approaches for the extension of SVM and similar classifiers to a multi-category framework, so that they can be used for the classification of data with more than two classes"-- Provided by publisher

Advanced materials with improved properties have the potential to fuel future technological advancements. However, identification and discovery of these optimal materials for a specific application is a non-trivial task, because of the vastness of the chemical search space with enormous compositional and configurational degrees of freedom. Materials informatics provides an efficient approach toward rational design of new materials, via learning from known data to make decisions on new and previously unexplored compounds in an accelerated manner. Here, we demonstrate the power and utility of such statistical learning (or machine learning, henceforth referred to as ML) via building a support vector machine (SVM) based classifier that uses elemental features (or descriptors) to predict the formability of a given ABX₃ halide composition (where A and B represent monovalent and divalent cations, respectively, and X is F, Cl, Br, or I anion) in the perovskite crystal structure. The classification model is built by learning from a dataset of 185 experimentally known ABX₃ compounds. After exploring a wide range of features, we identify ionic radii, tolerance factor, and octahedral factor to be the most important factors for the classification, suggesting that steric and geometric packing effects govern the stability of these halides. As a result, the trained and validated models then predict, with a high degree of confidence, several novel ABX₃ compositions with perovskite crystal structure.

In the process of macromolecular model building, crystallographers must examine electron density for isolated atoms and differentiate sites containing structured solvent molecules from those containing elemental ions. This task requires specific knowledge of metal-binding chemistry and scattering properties and is prone to error. A method has previously been described to identify ions based on manually chosen criteria for a number of elements. Here, the use of support vector machines (SVMs) to automatically classify isolated atoms as either solvent or one of various ions is described. Two data sets of protein crystal structures, one containing manually curated structures deposited with anomalous diffraction data and another with automatically filtered, high-resolution structures, were constructed. On the manually curated data set, an SVM classifier was able to distinguish calcium from manganese, zinc, iron and nickel, as well as all five of these ions from water molecules, with a high degree of accuracy. Additionally, SVMs trained on the automatically curated set of high-resolution structures were able to successfully classify most common elemental ions in an independent validation test set. This method is readily extensible to other elemental ions and can also be used in conjunction with previous methods based on a priori expectations of the chemical environment and X-ray scattering.

Reliability/safety analysis of stochastic dynamic systems (e.g., nuclear power plants, airplanes, chemical plants) is currently performed through a combination of Event-Tress and Fault-Trees. However, these conventional methods suffer from certain drawbacks: • Timing of events is not explicitly modeled • Ordering of events is preset by the analyst • The modeling of complex accident scenarios is driven by expert-judgment For these reasons, there is currently an increasing interest into the development of dynamic PRA methodologies since they can be used to address the deficiencies of conventional methods listed above.

• Introduction.- Generalized Eigenvalue Proximal Support Vector Machines.- Twin Support Vector Machines (TWSVM) for Classification.- TWSVR: Twin Support Vector Machine Based Regression.- Variants of Twin Support Vector Machines: Some More Formulations.- TWSVM for Unsupervised and Semi-Supervised Learning.- Some Additional Topics.- Applications Based on TWSVM.- References.
• (source: Nielsen Book Data)

This book provides a systematic and focused study of the various aspects of twin support vector machines (TWSVM) and related developments for classification and regression. In addition to presenting most of the basic models of TWSVM and twin support vector regression (TWSVR) available in the literature, it also discusses the important and challenging applications of this new machine learning methodology. A chapter on "Additional Topics" has been included to discuss kernel optimization and support tensor machine topics, which are comparatively new but have great potential in applications. It is primarily written for graduate students and researchers in the area of machine learning and related topics in computer science, mathematics, electrical engineering, management science and finance.
(source: Nielsen Book Data)

• Preface. PART I.
• 1 What is Knowledge Discovery? 1.1 Machine Learning. 1.2 The Structure of the Universe X. 1.3 Inductive Learning. 1.4 Model Representations. Exercises. Bibliographic Notes.
• 2 Knowledge Discovery Environments. 2.1 Computational Aspects of Knowledge Discovery. 2.1.1 Data Access. 2.1.2 Visualization. 2.1.3 Data Manipulation. 2.1.4 Model Building and Evaluation. 2.1.5 Model Deployment. 2.2 Other Toolsets. Exercises. Bibliographic Notes.
• 3 Describing Data Mathematically. 3.1 From Data Sets to Vector Spaces. 3.1.1 Vectors. 3.1.2 Vector Spaces. 3.2 The Dot Product as a Similarity Score. 3.3 Lines, Planes, and Hyperplanes. Exercises. Bibliographic Notes.
• 4 Linear Decision Surfaces and Functions. 4.1 From Data Sets to Decision Functions. 4.1.1 Linear Decision Surfaces through the Origin. 4.1.2 Decision Surfaces with an Offset Term. 4.2 A Simple Learning Algorithm. 4.3 Discussion. Exercises. Bibliographic Notes.
• 5 Perceptron Learning. 5.1 Perceptron Architecture and Training. 5.2 Duality. 5.3 Discussion. Exercises. Bibliographic Notes.
• 6 Maximum Margin Classifiers. 6.1 Optimization Problems. 6.2 Maximum Margins. 6.3 Optimizing the Margin. 6.4 Quadratic Programming. 6.5 Discussion. Exercises. Bibliographic Notes. PART II.
• 7 Support Vector Machines. 7.1 The Lagrangian Dual. 7.2 Dual MaximumMargin Optimization. 7.2.1 The Dual Decision Function. 7.3 Linear Support Vector Machines. 7.4 Non-Linear Support Vector Machines. 7.4.1 The Kernel Trick. 7.4.2 Feature Search. 7.4.3 A Closer Look at Kernels. 7.5 Soft-Margin Classifiers. 7.5.1 The Dual Setting for Soft-Margin Classifiers. 7.6 Tool Support. 7.6.1 WEKA. 7.6.2 R. 7.7 Discussion. Exercises. Bibliographic Notes.
• 8 Implementation. 8.1 Gradient Ascent. 8.1.1 The Kernel-Adatron Algorithm. 8.2 Quadratic Programming. 8.2.1 Chunking. 8.3 Sequential Minimal Optimization. 8.4 Discussion. Exercises. Bibliographic Notes.
• 9 Evaluating What has been Learned. 9.1 Performance Metrics. 9.1.1 The Confusion Matrix. 9.2 Model Evaluation. 9.2.1 The Hold-Out Method. 9.2.2 The Leave-One-Out Method. 9.2.3 N-Fold Cross-Validation. 9.3 Error Confidence Intervals. 9.3.1 Model Comparisons. 9.4 Model Evaluation in Practice. 9.4.1 WEKA. 9.4.2 R. Exercises. Bibliographic Notes.
• 10 Elements of Statistical Learning Theory. 10.1 The VC-Dimension and Model Complexity. 10.2 A Theoretical Setting for Machine Learning. 10.3 Empirical Risk Minimization. 10.4 VC-Confidence. 10.5 Structural Risk Minimization. 10.6 Discussion. Exercises. Bibliographic Notes. PART III.
• 11 Multi-Class Classification. 11.1 One-versus-the-Rest Classification. 11.2 Pairwise Classification. 11.3 Discussion. Exercises. Bibliographic Notes.
• 12 Regression with Support Vector Machines. 12.1 Regression as Machine Learning. 12.2 Simple and Multiple Linear Regression. 12.3 Regression with Maximum Margin Machines. 12.4 Regression with Support Vector Machines. 12.5 Model Evaluation. 12.6 Tool Support. 12.6.1 WEKA. 12.6.2 R. Exercises. Bibliographic Notes.
• 13 Novelty Detection. 13.1 Maximum Margin Machines. 13.2 The Dual Setting. 13.3 Novelty Detection in R. Exercises. Bibliographic Notes. Appendix A: Notation. Appendix B: A Tutorial Introduction to R. B.1 Programming Constructs. B.2 Data Constructs. B.3 Basic Data Analysis. Bibliographic Notes. References. Index.
• (source: Nielsen Book Data)

An easy-to-follow introduction to support vector machines This book provides an in-depth, easy-to-follow introduction to support vector machines drawing only from minimal, carefully motivated technical and mathematical background material. It begins with a cohesive discussion of machine learning and goes on to cover: Knowledge discovery environments Describing data mathematically Linear decision surfaces and functions Perceptron learning Maximum margin classifiers Support vector machines Elements of statistical learning theory Multi-class classification Regression with support vector machines Novelty detection Complemented with hands-on exercises, algorithm descriptions, and data sets, Knowledge Discovery with Support Vector Machines is an invaluable textbook for advanced undergraduate and graduate courses. It is also an excellent tutorial on support vector machines for professionals who are pursuing research in machine learning and related areas.
(source: Nielsen Book Data)

Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)-classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which.

• Introduction
• Preliminaries
• Histogram Rule: Oracle Inequality and Learning Rates
• Localized SVMs: Oracle Inequalities and Learning Rates
• Discussion.

Support vector machines (SVMs) are one of the most successful algorithms on small and medium-sized data sets, but on large-scale data sets their training and predictions become computationally infeasible. The author considers a spatially defined data chunking method for large-scale learning problems, leading to so-called localized SVMs, and implements an in-depth mathematical analysis with theoretical guarantees, which in particular include classification rates. The statistical analysis relies on a new and simple partitioning based technique and takes well-known margin conditions into account that describe the behavior of the data-generating distribution. It turns out that the rates outperform known rates of several other learning algorithms under suitable sets of assumptions. From a practical point of view, the author shows that a common training and validation procedure achieves the theoretical rates adaptively, that is, without knowing the margin parameters in advance.
(source: Nielsen Book Data)

• Introduction Reproducing Kernel Banach Spaces Generalized Mercer Kernels Positive Definite Kernels Support Vector Machines Concluding Remarks Acknowledgments Index Bibliography.
• (source: Nielsen Book Data)

This article studies constructions of reproducing kernel Banach spaces (RKBSs) which may be viewed as a generalization of reproducing kernel Hilbert spaces (RKHSs). A key point is to endow Banach spaces with reproducing kernels such that machine learning in RKBSs can be well-posed and of easy implementation. First the authors verify many advanced properties of the general RKBSs such as density, continuity, separability, implicit representation, imbedding, compactness, representer theorem for learning methods, oracle inequality, and universal approximation. Then, they develop a new concept of generalized Mercer kernels to construct $p$-norm RKBSs for $1\leq p\leq\infty$.
(source: Nielsen Book Data)

• Support Vector Machines for Classification Kernel-based Models Learning with Kernels.
• (source: Nielsen Book Data)

Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data.
(source: Nielsen Book Data)

• Introduction to Statistical Machine Learning Overview of Semi-Supervised Learning Mixture Models and EM Co-Training Graph-Based Semi-Supervised Learning Semi-Supervised Support Vector Machines Human Semi-Supervised Learning Theory and Outlook.
• (source: Nielsen Book Data)

Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field.
(source: Nielsen Book Data)

• Support Vector Machines.- Evolutionary Algorithms.- Support Vector Machines and Evolutionary Algorithms.
• (source: Nielsen Book Data)

When discussing classification, support vector machines are known to be a capable and efficient technique to learn and predict with high accuracy within a quick time frame. Yet, their black box means to do so make the practical users quite circumspect about relying on it, without much understanding of the how and why of its predictions. The question raised in this book is how can this 'masked hero' be made more comprehensible and friendly to the public: provide a surrogate model for its hidden optimization engine, replace the method completely or appoint a more friendly approach to tag along and offer the much desired explanations? Evolutionary algorithms can do all these and this book presents such possibilities of achieving high accuracy, comprehensibility, reasonable runtime as well as unconstrained performance.
(source: Nielsen Book Data)

• Introduction Linear Support Vector Machines Nonlinear Support Vector Machines Advanced Topics Support Vector Machines for Beamforming Determination of Angle of Arrival Other Applications in Electromagnetics.
• (source: Nielsen Book Data)

Since the 1990s there has been significant activity in the theoretical development and applications of Support Vector Machines (SVMs). The theory of SVMs is based on the cross-pollenization of optimization theory, statistical learning, kernel theory, and algorithmics. So far, machine learning has largely been devoted to solving problems relating to data mining, text categorization, and pattern/facial recognition but not so much in the field of electromagnetics. Recently, however, popular binary machine learning algorithms, including support vector machines (SVM), have successfully been applied to wireless communication problems, notably spread spectrum receiver design and channel equalization. The aim of this book is to gently introduce support vector machines in its linear and non linear form, both as regressors and as classifiers, and to show how they can be applied to several antenna array processing problems and electromagnetics in general. The lecture is divided into three main parts. The first three chapters cover the theory of SVMS, both as classifiers and regressors. The next three chapters deal with applications in antenna array processing and other areas in electromagnetics. The four appendices at the end of the book comprise the last part. The inclusion of MATLAB files will help readers start their application of the algorithms covered in the book.
(source: Nielsen Book Data)
Support Vector Machines (SVM) were introduced in the early 90's as a novel nonlinear solution for classification and regression tasks. These techniques have been proved to have superior performances in a large variety of real world applications due to their generalization abilities and robustness against noise and interferences. This book introduces a set of novel techniques based on SVM that are applied to antenna array processing and electromagnetics. In particular, it introduces methods for linear and nonlinear beamforming and parameter design for arrays and electromagnetic applications.
(source: Nielsen Book Data)

The aqueous extract of yerba mate, a South American tea beverage made from Ilex paraguariensis leaves, has demonstrated bactericidal and inhibitory activity against bacterial pathogens, including methicillin-resistant Staphylococcus aureus (MRSA). In this paper, the gas chromatography-mass spectrometry (GC-MS) analysis of two unique fractions of yerba mate aqueous extract revealed 8 identifiable small molecules in those fractions with antimicrobial activity. For a more comprehensive analysis, a data analysis pipeline was assembled to prioritize compounds for antimicrobial testing against both MRSA and methicillin-sensitive S. aureus using forty-two unique fractions of the tea extract that were generated in duplicate, assayed for activity, and analyzed with GC-MS. As validation of our automated analysis, we checked our predicted active compounds for activity in literature references and used authentic standards to test for antimicrobial activity. 3,4-dihydroxybenzaldehyde showed the most antibacterial activity against MRSA at low concentrations in our bioassays. In addition, quinic acid and quercetin were identified using random forests analysis and 5-hydroxy pipecolic acid was identified using linear discriminant analysis. We also generated a ranked list of unidentified compounds that may contribute to the antimicrobial activity of yerba mate against MRSA. Finally, here we utilized GC-MS data to implement an automated analysis that resulted in a ranked list of compounds that likely contribute to the antimicrobial activity of aqueous yerba mate extract against MRSA.

• Support Vector Machines for Classification Kernel-based Models Learning with Kernels.
• (source: Nielsen Book Data)

Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data.
(source: Nielsen Book Data)