Titlebook: Dimensionality Reduction with Unsupervised Nearest Neighbors; Oliver Kramer Book 2013 Springer-Verlag Berlin Heidelberg 2013 Computational

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Sozialwissenschaftliche Konflikttheorienthods have been introduced in the past. For large data sets, efficient methods are required. With UNN and its variants, we have introduced a fast and efficient dimensionality reduction method. All UNN variants compute an embedding in .(..) and can be accelerated to .(. log.), when space partitioning

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Book 2013, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustrate the introduced concepts and to highlight the experimental results.. .

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1868-4394 ta sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustrate the introduced concepts and to highlight the experimental results.. .978-3-662-51895-3978-3-642-38652-7Series ISSN 1868-4394 Series E-ISSN 1868-4408

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Dimensionality Reduction with Unsupervised Nearest Neighbors

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Silke L. Schneider,Verena Ortmannsraphs like breadth-first and depth-first search to advanced reinforcement strategies for learning of complex behaviors in uncertain environments. Many AI research objectives aim at the solution of special problem classes. Subareas like speech processing have shown impressive achievements in recent years that come close to human abilities.

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Sozialwissenschaftliche Forschung und Praxisdimensions. Variants for multi-label classification, regression, and semi supervised learning settings allow the application to a broad spectrum of machine learning problems. Decision theory gives valuable insights into the characteristics of nearest neighbor learning results.

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