| Description |
1 online resource (xv, 126 pages) |
| Contents |
Introduction -- Preliminaries -- Histogram Rule: Oracle Inequality and Learning Rates -- Localized SVMs: Oracle Inequalities and Learning Rates -- Discussion |
| Summary |
Support vector machines (SVMs) are one of the most successful algorithms on small and medium-sized data sets, but on large-scale data sets their training and predictions become computationally infeasible. The author considers a spatially defined data chunking method for large-scale learning problems, leading to so-called localized SVMs, and implements an in-depth mathematical analysis with theoretical guarantees, which in particular include classification rates. The statistical analysis relies on a new and simple partitioning based technique and takes well-known margin conditions into account that describe the behavior of the data-generating distribution. It turns out that the rates outperform known rates of several other learning algorithms under suitable sets of assumptions. From a practical point of view, the author shows that a common training and validation procedure achieves the theoretical rates adaptively, that is, without knowing the margin parameters in advance |
| Bibliography |
Includes bibliographical references |
| Notes |
Print version record |
| Subject |
Discriminant analysis.
|
|
Support vector machines.
|
|
Discriminant Analysis
|
|
Análisis discriminante
|
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Discriminant analysis
|
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Support vector machines
|
| Form |
Electronic book
|
| ISBN |
9783658295912 |
|
3658295910 |
|
