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Focusing on a novel dimensionality reduction technique, the book explores unsupervised nearest neighbors (UNN) as a method for enhancing classification and regression tasks. It begins with foundational machine learning concepts and a practical application in the energy sector. The text systematically develops various UNN models, addressing challenges like incomplete data and noise, while comparing different optimization strategies, including evolutionary and swarm-based methods. Richly illustrated with color figures, it presents experimental results that showcase UNN's effectiveness in both synthetic and real-world datasets.
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Dimensionality Reduction with Unsupervised Nearest Neighbors, Oliver Kramer
- Langue
- Année de publication
- 2017
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- (souple)
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- Titre
- Dimensionality Reduction with Unsupervised Nearest Neighbors
- Langue
- Anglais
- Auteurs
- Oliver Kramer
- Éditeur
- Springer, Berlin
- Publié
- 2017
- Format
- souple
- Pages
- 132
- ISBN13
- 9783662518953
- Séries
- Mots clés
- Nonfiction, Science et Mathématiques, Mathématiques
- Description
- Focusing on a novel dimensionality reduction technique, the book explores unsupervised nearest neighbors (UNN) as a method for enhancing classification and regression tasks. It begins with foundational machine learning concepts and a practical application in the energy sector. The text systematically develops various UNN models, addressing challenges like incomplete data and noise, while comparing different optimization strategies, including evolutionary and swarm-based methods. Richly illustrated with color figures, it presents experimental results that showcase UNN's effectiveness in both synthetic and real-world datasets.