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- Ruim aanbod met 7 miljoen producten
Omschrijving
Similar to other data mining and machine learning tasks, multi-label learning suffers from dimensionality. An effective way to mitigate this problem is through dimensionality reduction, which extracts a small number of features by removing irrelevant, redundant, and noisy information. The data mining and machine learning literature currently lacks a unified treatment of multi-label dimensionality reduction that incorporates both algorithmic developments and applications.
Addressing this shortfall, Multi-Label Dimensionality Reduction covers the methodological developments, theoretical properties, computational aspects, and applications of many multi-label dimensionality reduction algorithms. It explores numerous research questions, including:
- How to fully exploit label correlations for effective dimensionality reduction
- How to scale dimensionality reduction algorithms to large-scale problems
- How to effectively combine dimensionality reduction with classification
- How to derive sparse dimensionality reduction algorithms to enhance model interpretability
- How to perform multi-label dimensionality reduction effectively in practical applications
The authors emphasize their extensive work on dimensionality reduction for multi-label learning. Using a case study of Drosophila gene expression pattern image annotation, they demonstrate how to apply multi-label dimensionality reduction algorithms to solve real-world problems. A supplementary website provides a MATLAB(R) package for implementing popular dimensionality reduction algorithms.
Specificaties
Betrokkenen
- Auteur(s):
- Uitgeverij:
Inhoud
- Aantal bladzijden:
- 208
- Taal:
- Engels
- Reeks:
Eigenschappen
- Productcode (EAN):
- 9781439806159
- Verschijningsdatum:
- 4/11/2013
- Uitvoering:
- Hardcover
- Formaat:
- Genaaid
- Afmetingen:
- 152 mm x 236 mm
- Gewicht:
- 635 g
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