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Dimensionality Reduction with Unsupervised Nearest Neighbors

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  • 132 stránok
  • 5 hodin čítania

Viac o knihe

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.

Vydanie

Nákup knihy

Dimensionality Reduction with Unsupervised Nearest Neighbors, Oliver Kramer

Jazyk
Rok vydania
2017
Väzba
(mäkká)
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Titul
Dimensionality Reduction with Unsupervised Nearest Neighbors
Jazyk
anglicky
Rok vydania
2017
Väzba
mäkká
Počet strán
132
ISBN13
9783662518953
Série
Štítky
Anotácia
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.