Support Vector Machines for Pattern Classification (Record no. 13064)
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| -- | 9781846282195 |
| -- | 978-1-84628-219-5 |
| 024 7# - | |
| -- | 10.1007/1-84628-219-5 |
| -- | doi |
| 050 #4 - | |
| -- | Q337.5 |
| 050 #4 - | |
| -- | TK7882.P3 |
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| -- | UYQP |
| -- | bicssc |
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| -- | COM016000 |
| -- | bisacsh |
| 082 04 - | |
| -- | 006.4 |
| -- | 23 |
| 100 1# - | |
| -- | Abe, Shigeo. |
| -- | author. |
| 245 10 - | |
| -- | Support Vector Machines for Pattern Classification |
| -- | [electronic resource] / |
| -- | by Shigeo Abe. |
| 264 #1 - | |
| -- | London : |
| -- | Springer London, |
| -- | 2005. |
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| -- | XIV, 343p. 110 illus. |
| -- | online resource. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
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| -- | computer |
| -- | c |
| -- | rdamedia |
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| -- | online resource |
| -- | cr |
| -- | rdacarrier |
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| -- | text file |
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| -- | rda |
| 490 1# - | |
| -- | Advances in Pattern Recognition |
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| -- | Two-Class Support Vector Machines -- Multiclass Support Vector Machines -- Variants of Support Vector Machines -- Training Methods -- Feature Selection and Extraction -- Clustering -- Kernel-Based Methods -- Maximum-Margin Multilayer Neural Networks -- Maximum-Margin Fuzzy Classifiers -- Function Approximation. |
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| -- | I was shocked to see a student’s report on performance comparisons between support vector machines (SVMs) and fuzzy classi?ers that we had developed withourbestendeavors.Classi?cationperformanceofourfuzzyclassi?erswas comparable, but in most cases inferior, to that of support vector machines. This tendency was especially evident when the numbers of class data were small. I shifted my research e?orts from developing fuzzy classi?ers with high generalization ability to developing support vector machine–based classi?ers. This book focuses on the application of support vector machines to p- tern classi?cation. Speci?cally, we discuss the properties of support vector machines that are useful for pattern classi?cation applications, several m- ticlass models, and variants of support vector machines. To clarify their - plicability to real-world problems, we compare performance of most models discussed in the book using real-world benchmark data. Readers interested in the theoretical aspect of support vector machines should refer to books such as [109, 215, 256, 257]. |
| 650 #0 - | |
| -- | Computer science. |
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| -- | Artificial intelligence. |
| 650 #0 - | |
| -- | Text processing (Computer science. |
| 650 #0 - | |
| -- | Optical pattern recognition. |
| 650 14 - | |
| -- | Computer Science. |
| 650 24 - | |
| -- | Pattern Recognition. |
| 650 24 - | |
| -- | Document Preparation and Text Processing. |
| 650 24 - | |
| -- | Artificial Intelligence (incl. Robotics). |
| 650 24 - | |
| -- | Control Engineering. |
| 710 2# - | |
| -- | SpringerLink (Online service) |
| 773 0# - | |
| -- | Springer eBooks |
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| -- | Printed edition: |
| -- | 9781852339296 |
| 830 #0 - | |
| -- | Advances in Pattern Recognition |
| 856 40 - | |
| -- | http://dx.doi.org/10.1007/1-84628-219-5 |
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| -- | ZDB-2-SCS |
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| -- | Computer Science (Springer-11645) |
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| -- | 13064 |
| -- | 13064 |
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