Twin Support Vector Machines : Models, Extensions and Applications / by Jayadeva, Reshma Khemchandani, Suresh Chandra.
By: Jayadeva [author.].
Contributor(s): Khemchandani, Reshma [author.] | Chandra, Suresh [author.].
Material type: TextSeries: Studies in Computational Intelligence: 659Publisher: New York : Springer, c2017Description: xiv, 211 p. ; 23 cm.ISBN: 9783319834627; 9783319461847; 9783319461861 (ebook).Subject(s): Engineering | Artificial intelligence | Computational intelligence | Engineering | Computational Intelligence | Artificial Intelligence (incl. Robotics) | Artificial intelligence | Computational intelligence | EngineeringDDC classification: 006.3Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Books |
Prof. G. K. Chadha Library
South Asian University |
006.3 J425t (Browse shelf) | Not For Loan | BK00012664 | ||
Books |
Prof. G. K. Chadha Library
South Asian University |
006.3 J425t (Browse shelf) | Available | BK00012663 |
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006.3 H2887 Harmony search and nature inspired optimization algorithms : | 006.3 H311m Machine learning in action / | 006.3 H992 Hybrid intelligent systems : | 006.3 J425t Twin Support Vector Machines : | 006.3 J425t Twin Support Vector Machines : | 006.3 K1892u Understanding neural networks and fuzzy logic : | 006.3 K1892u Understanding neural networks and fuzzy logic : |
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.
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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.