Support Vector Machines: Evolution and Applications

$160.00

Series: Computer Science, Technology and Applications
BISAC: COM014000

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.

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Table of Contents

Table of Contents

Preface

Acknowledgements

Chapter 1. Introduction to Support Vector Machines
(Pooja Saigal, PhD, Vivekananda School of Information Technology, Vivekananda Institute of Professional Studies, New Delhi, India)

Chapter 2. 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)

Chapter 3. 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)

Chapter 4. Hardware Architectures of Support Vector Machine Applied in Pattern Recognition Systems
(Gracieth Cavalcanti Batista, Duarte Lopes de Oliveira, Washington Luis Santos Silva and Osamu Saotome, Department of Eletronic Devices and Systems, Technological Institute of Aeronautics, São José dos Campos, SP, Brazil, and others)

Chapter 5. Speaker Recognition Using Support Vector Machine
(Nivedita Palia, Deepali Kamthani and Shri Kant, School of Information Technology, Vivekananda Institute of Professional Studies, Guru Gobind Singh Indraprastha University, Delhi, India, and others)

Chapter 6. Application of Support Vector Machine (SVM) in Classification of Iron Ores in Mines
(Ashok Kumar Patel, Snehamoy Chatterjee and Amit Kumar Gorai, Department of Computer Science and Engineering, C. V. Raman Global University, Bhubaneswar, Orissa, India)

Chapter 7. Multi-Category Classification
(Pooja Saigal, PhD, Vivekananda School of Information Technology, Vivekananda Institute of Professional Studies, New Delhi, India)

Chapter 8. Simultaneous Prediction of the Density and Viscosity for the Ternary System Water-Ethanol–Ethylene Glycol Ionic Liquids Using Support Vector Machine
(Ehsan Kianfar, Saeed Hajimirzaee and Reza Azimikia, Department of Chemical Engineering, Arak Branch, Islamic Azad University, Arak, Iran, and others)

About the Editor

Dr. Pooja Saigal is an Associate Professor in Vivekananda Institute of Professional Studies, affiliated with Guru Gobind Singh Indraprastha University, New Delhi. She received her PhD in Computer Science from South Asian University (established by SAARC), New Delhi in 2018. Her research interests include Machine Learning, Optimization and Image Processing. She has proposed various supervised, unsupervised and semi-supervised machine learning algorithms and applied them on image processing problems like content-based image retrieval, segmentation etc. She is UGC-NET qualified in Computer Science. She holds a Master’s Degree (2004) and Bachelor’s Degree (2001) in Computer Applications. She has authored more than 20 research papers in international journals, books and conference proceedings.

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