| 书目名称 | Dimensionality Reduction with Unsupervised Nearest Neighbors |
| 编辑 | Oliver Kramer |
| 视频video | |
| 概述 | Presents recent research in the Hybridization of Metaheuristics for Optimization Problems.State-of-the-Art book.Written from a leading expert in this field |
| 丛书名称 | Intelligent Systems Reference Library |
| 图书封面 |  |
| 描述 | .This book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, 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.. . |
| 出版日期 | Book 2013 |
| 关键词 | Computational Intelligence; Evolutionary Computation; Self-Adaptive Heuristics |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-642-38652-7 |
| isbn_softcover | 978-3-662-51895-3 |
| isbn_ebook | 978-3-642-38652-7Series ISSN 1868-4394 Series E-ISSN 1868-4408 |
| issn_series | 1868-4394 |
| copyright | Springer-Verlag Berlin Heidelberg 2013 |
发表于 2025-3-21 18:40:58
