- Authors:
- Jayadeva,
- Reshma Khemchandani,
- Suresh Chandra
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.
Cited By
- Mir A and Nasiri J (2018). KNN-based least squares twin support vector machine for pattern classification, Applied Intelligence, 48:12, (4551-4564), Online publication date: 1-Dec-2018.
- Rastogi (ne Khemchandani) R, Saigal P and Chandra S (2018). Angle-based twin parametric-margin support vector machine for pattern classification, Knowledge-Based Systems, 139:C, (64-77), Online publication date: 1-Jan-2018.
- Veresnikov G and Skryabin A (2017). The development of data mining methods criteria to identify failures of aircraft control surface actuator, Procedia Computer Science, 112:C, (1007-1014), Online publication date: 1-Sep-2017.
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