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- Author: Ingrid Karin Blaschzyk
- Publisher: Springer Spektrum
- ISBN-10: 3658295902
- ISBN-13: 9783658295905
- Format: 14.8 x 21 x 0.8 cm, minkšti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
Improved Classification Rates for Localized Algorithms Under Margin Conditions (e-book) (used book) | bookbook.eu
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Description
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.
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- Author: Ingrid Karin Blaschzyk
- Publisher: Springer Spektrum
- ISBN-10: 3658295902
- ISBN-13: 9783658295905
- Format: 14.8 x 21 x 0.8 cm, minkšti viršeliai
- Language: English English
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.
Reviews