Support vector machines theory and applications /
"The support vector machine (SVM) has become one of the standard tools for machine learning and data mining. This volume presents the state of the art of the mathematical foundation of SVM in statistical learning theory, as well as noel algorithms and applications. "Support Vector Machines...
| Other Authors: | Wang, Lipo., SpringerLink (Online service) |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Berlin ; New York :
Springer,
©2005.
Berlin ; New York : [2005] |
| Physical Description: |
1 online resource (x, 431 pages) : figure (some color), table. |
| Series: |
Studies in fuzziness and soft computing ;
v. 177. |
| Subjects: |
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| 245 | 0 | 0 | |a Support vector machines : |b theory and applications / |c Lipo Wang (ed.). |
| 260 | |a Berlin ; |a New York : |b Springer, |c ©2005. | ||
| 264 | 1 | |a Berlin ; |a New York : |b Springer, |c [2005] | |
| 264 | 4 | |c ©2005. | |
| 300 | |a 1 online resource (x, 431 pages) : |b figure (some color), table. | ||
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| 490 | 1 | |a Studies in fuzziness and soft computing, |x 1434-9922 ; |v v. 177. | |
| 504 | |a Includes bibliographical references and index. | ||
| 520 | 1 | |a "The support vector machine (SVM) has become one of the standard tools for machine learning and data mining. This volume presents the state of the art of the mathematical foundation of SVM in statistical learning theory, as well as noel algorithms and applications. "Support Vector Machines" provides a selection of numerous real-world applications, such as bioinformatics, text categorization, pattern recognition, and object detection, written by leading experts in the respective fields."--Jacket. | |
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a From the contents: Support Vector Machines -- An Introduction -- Multiple Model Estimation for Nonlinear Classification -- Componentwise Least Squares Support Vector Machines -- Active Support Vector Learning with Statistical Queries -- Local Learning vs. Global Learning: An Introduction to Maxi-Min Margin Machine -- Active-Set Methods for Support Vector Machines -- Theoretical and Practical Model Selection Methods for Support Vector Classifiers -- Adaptive Discriminant and Quasiconformal Kernel Nearest Neighbor Classification -- Improving the Performance of the Support Vector Machine: Two Geometrical Scaling Methods -- An Accelerated Robust Support Vector Machine Algorithm -- Fuzzy Support Vector Machines with Automatic Membership Setting -- Iterative Single Data Algorithm for Training Kernel Machines from Huge Data Sets: Theory and Performance -- Kernel Discriminant Learning with Application to Face Recognition -- Fast Color Texture-based Object Detection in Images: Application to License Plate Localization. | |
| 506 | |a University staff and students only. Requires University Computer Account login off-campus. | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Pattern recognition systems. | |
| 650 | 2 | |a Data Mining. | |
| 650 | 2 | |a Pattern Recognition, Automated. | |
| 650 | 2 | |a Machine Learning. | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Reconnaissance des formes (Informatique) | |
| 650 | 0 | 7 | |a Data mining. |2 cct. |
| 650 | 0 | 7 | |a Pattern recognition systems. |2 cct. |
| 650 | 0 | 7 | |a Exploration de données (Informatique) |2 cct. |
| 650 | 0 | 7 | |a Apprentissage automatique. |2 cct. |
| 650 | 0 | 7 | |a Reconnaissance des formes (informatique) . |2 cct. |
| 650 | 0 | 7 | |a Machine learning. |2 cct. |
| 650 | 7 | |a Ingénierie. |2 eclas. | |
| 650 | 7 | |a Data mining. |2 fast. | |
| 650 | 7 | |a Machine learning. |2 fast. | |
| 650 | 7 | |a Pattern recognition systems. |2 fast. | |
| 700 | 1 | |a Wang, Lipo. | |
| 710 | 2 | |a SpringerLink (Online service) | |
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