標(biāo)題: Titlebook: Dimensionality Reduction with Unsupervised Nearest Neighbors; Oliver Kramer Book 2013 Springer-Verlag Berlin Heidelberg 2013 Computational [打印本頁(yè)] 作者: oxidation 時(shí)間: 2025-3-21 18:40
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書(shū)目名稱Dimensionality Reduction with Unsupervised Nearest Neighbors讀者反饋
書(shū)目名稱Dimensionality Reduction with Unsupervised Nearest Neighbors讀者反饋學(xué)科排名
作者: insurrection 時(shí)間: 2025-3-21 21:02
K-Nearest Neighborst part to play in this book. The chapter starts with an introduction to foundations in machine learning and decision theory with a focus on classification and regression. For the model selection problem, basic methods like cross-validation are introduced. Nearest neighbor methods are based on the la作者: 處理 時(shí)間: 2025-3-22 03:11
Ensemble Learninghbor and SVM classifiers and analyze its performance in a real-world application [69]. The ensembles are hybrids of . nearest neighbors classifiers that are based on averaging labels in the neighborhood of unknown patterns and the . SVMs that use separating hyperplanes.作者: 跳動(dòng) 時(shí)間: 2025-3-22 06:30 作者: Antagonist 時(shí)間: 2025-3-22 10:43 作者: 茁壯成長(zhǎng) 時(shí)間: 2025-3-22 13:16 作者: 茁壯成長(zhǎng) 時(shí)間: 2025-3-22 20:07 作者: 破裂 時(shí)間: 2025-3-22 21:51 作者: FER 時(shí)間: 2025-3-23 02:20
Oliver KramerPresents recent research in the Hybridization of Metaheuristics for Optimization Problems.State-of-the-Art book.Written from a leading expert in this field作者: 萬(wàn)靈丹 時(shí)間: 2025-3-23 06:42 作者: Mendacious 時(shí)間: 2025-3-23 10:26 作者: entice 時(shí)間: 2025-3-23 17:28
978-3-662-51895-3Springer-Verlag Berlin Heidelberg 2013作者: Indelible 時(shí)間: 2025-3-23 21:29 作者: 微枝末節(jié) 時(shí)間: 2025-3-24 01:50
Dimensionality Reduction with Unsupervised Nearest Neighbors978-3-642-38652-7Series ISSN 1868-4394 Series E-ISSN 1868-4408 作者: 減弱不好 時(shí)間: 2025-3-24 04:19 作者: NOT 時(shí)間: 2025-3-24 07:49 作者: GRIEF 時(shí)間: 2025-3-24 13:28
Sozialwissenschaftliche Forschung und Praxist part to play in this book. The chapter starts with an introduction to foundations in machine learning and decision theory with a focus on classification and regression. For the model selection problem, basic methods like cross-validation are introduced. Nearest neighbor methods are based on the la作者: Canvas 時(shí)間: 2025-3-24 14:51 作者: 外形 時(shí)間: 2025-3-24 20:37 作者: Condescending 時(shí)間: 2025-3-25 01:07 作者: PALL 時(shí)間: 2025-3-25 05:08 作者: 討人喜歡 時(shí)間: 2025-3-25 07:46 作者: GEM 時(shí)間: 2025-3-25 12:21
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作者: Capitulate 時(shí)間: 2025-3-25 17:48
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..?.作者: 轉(zhuǎn)折點(diǎn) 時(shí)間: 2025-3-25 22:08
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 作者: atrophy 時(shí)間: 2025-3-26 01:25
Dimensionality Reduction with Unsupervised Nearest Neighbors作者: 不朽中國(guó) 時(shí)間: 2025-3-26 07:28
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.作者: 奴才 時(shí)間: 2025-3-26 11:33
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.作者: kindred 時(shí)間: 2025-3-26 12:44 作者: Indolent 時(shí)間: 2025-3-26 20:24 作者: 熱情的我 時(shí)間: 2025-3-26 22:50
Sozialwissenschaftliche Konflikttheorienhe high-dimensional data space in latent space. The variants reach from sorting approaches in 1-dimensional latent spaces to submanifold learning in continuous latent spaces with separate parameterizations for each model. In the following, we summarize the most important results of this work.作者: lymphedema 時(shí)間: 2025-3-27 01:44
Introduction,raphs 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.作者: 粗糙 時(shí)間: 2025-3-27 07:17
K-Nearest Neighborsdimensions. 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.作者: GRE 時(shí)間: 2025-3-27 11:24
Latent Sorting closest embedded patterns. All presented methods will be analyzed experimentally. In the remainder of this book, various optimization strategies for UNN will be introduced, and the approach will be extended step by step.作者: 禁止,切斷 時(shí)間: 2025-3-27 17:32
Kernel and Submanifold Learningter handle non-linearities and high-dimensional data spaces. Experimental studies show that kernel unsupervised nearest neighbors (KUNN) is an efficient method for embedding high-dimensional patterns.作者: Crumple 時(shí)間: 2025-3-27 18:52 作者: Bereavement 時(shí)間: 2025-3-27 23:18
Dimensionality Reductionings. Dimensionality reduction can be employed for various tasks, e.g., visualization, preprocessing for pattern recognition methods, or for symbolic algorithms. To allow human understanding and interpretation of high-dimensional data, the reduction to 2- and 3-dimensional spaces is an important task.作者: 抒情短詩(shī) 時(shí)間: 2025-3-28 02:33
Metaheuristicsl embedding. We compare a discrete evolutionary approach based on stochastic swaps to a continuous evolutionary variant that is based on evolution strategies, i.e., the covariance matrix adaptation variant CMA-ES. The continuous variant is the first step to embeddings into continuous latent spaces.作者: 生意行為 時(shí)間: 2025-3-28 06:17
Book 2013nd 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,作者: 爆炸 時(shí)間: 2025-3-28 10:28 作者: Champion 時(shí)間: 2025-3-28 17:07
Sozialwissenschaftliche Hermeneutikings. Dimensionality reduction can be employed for various tasks, e.g., visualization, preprocessing for pattern recognition methods, or for symbolic algorithms. To allow human understanding and interpretation of high-dimensional data, the reduction to 2- and 3-dimensional spaces is an important task.作者: 夸張 時(shí)間: 2025-3-28 19:38 作者: 易于出錯(cuò) 時(shí)間: 2025-3-28 23:35
