標題: Titlebook: Dimensionality Reduction in Data Science; Max Garzon,Ching-Chi Yang,Lih-Yuan Deng Book 2022 The Editor(s) (if applicable) and The Author(s [打印本頁] 作者: Affordable 時間: 2025-3-21 19:39
書目名稱Dimensionality Reduction in Data Science影響因子(影響力)
書目名稱Dimensionality Reduction in Data Science影響因子(影響力)學科排名
書目名稱Dimensionality Reduction in Data Science網絡公開度
書目名稱Dimensionality Reduction in Data Science網絡公開度學科排名
書目名稱Dimensionality Reduction in Data Science被引頻次
書目名稱Dimensionality Reduction in Data Science被引頻次學科排名
書目名稱Dimensionality Reduction in Data Science年度引用
書目名稱Dimensionality Reduction in Data Science年度引用學科排名
書目名稱Dimensionality Reduction in Data Science讀者反饋
書目名稱Dimensionality Reduction in Data Science讀者反饋學科排名
作者: 躺下殘殺 時間: 2025-3-21 22:06 作者: indices 時間: 2025-3-22 00:30
Conventional Statistical Approaches,d from the dataset. Methods include Principal Component Analysis (PCA) and its variants, Independent component analysis and Discriminant Analysis. Linear algebra methods offer other approaches, including Singular value Decomposition (SVD) and Nonnegative Matrix Factorization (NMF).作者: Orchiectomy 時間: 2025-3-22 04:47
Information-Theoretic Approaches,rprisingly interesting reductions. This chapter discusses five major variations of this idea, including comparisons using the concept of mutual information previously used in statistics and machine learning.作者: 豐滿有漂亮 時間: 2025-3-22 10:58
Molecular Computing Approaches, leveraged to render several variations of this theme. They can be used obviously with genomic data, but perhaps surprisingly, with ordinary abiotic data just as well. Two major families of techniques of this kind are reviewed, namely genomic and pmeric coordinate systems for dimensionality reduction and data analysis.作者: 成績上升 時間: 2025-3-22 15:19
Statistical Learning Approaches,et variable based on various statistical solution methods. This chapter describes methods using linear regression and regularization that afford solutions to dimensionality reduction and solutions to problems that are explainable to humans.作者: 成績上升 時間: 2025-3-22 19:47 作者: 貪婪的人 時間: 2025-3-23 00:29 作者: iodides 時間: 2025-3-23 01:28
Geometric Approaches,called manifold) that can be fitted to the data while trying to minimize the deformations of distances as much as possible. Four major methods of this kind are reviewed, namely MDS, ISOMAP, .-., and random projections.作者: 大笑 時間: 2025-3-23 05:52 作者: 帶來的感覺 時間: 2025-3-23 11:09 作者: avulsion 時間: 2025-3-23 15:03 作者: 無價值 時間: 2025-3-23 20:17 作者: 神經 時間: 2025-3-23 23:27 作者: Magnificent 時間: 2025-3-24 05:18
Hybrid Debugging of Java Programsrprisingly interesting reductions. This chapter discusses five major variations of this idea, including comparisons using the concept of mutual information previously used in statistics and machine learning.作者: pancreas 時間: 2025-3-24 06:34
Yves Wautelet,Manuel Kolp,Stephan Poelmans leveraged to render several variations of this theme. They can be used obviously with genomic data, but perhaps surprisingly, with ordinary abiotic data just as well. Two major families of techniques of this kind are reviewed, namely genomic and pmeric coordinate systems for dimensionality reduction and data analysis.作者: ABHOR 時間: 2025-3-24 11:57 作者: Infiltrate 時間: 2025-3-24 16:39 作者: 官僚統(tǒng)治 時間: 2025-3-24 19:53 作者: 怪物 時間: 2025-3-24 23:40
978-3-031-05373-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: oblique 時間: 2025-3-25 04:49 作者: GLARE 時間: 2025-3-25 08:30 作者: Encephalitis 時間: 2025-3-25 15:06 作者: 音樂會 時間: 2025-3-25 18:49
Social Protection in Latin Americaspace. Statistical methods aim to preserve characteristic parameters such as mean, variance, and covariance of features in the population, as estimated from the dataset. Methods include Principal Component Analysis (PCA) and its variants, Independent component analysis and Discriminant Analysis. Lin作者: 偏狂癥 時間: 2025-3-25 20:13
Global Dynamics of Social Policyr of features. After the classical PCA that fits a linear (flat) subspace so that the total sum of squared distances of the data from the subspace (errors) is minimized, any distance function in this space can be used to endow it with a geometric structure, where ordinary intuition can be particular作者: 爵士樂 時間: 2025-3-26 03:56 作者: AXIS 時間: 2025-3-26 05:44
Yves Wautelet,Manuel Kolp,Stephan Poelmansant features from raw datasets for the purpose of extreme dimensionality reduction and solution efficiency. After describing the deep structure, it is leveraged to render several variations of this theme. They can be used obviously with genomic data, but perhaps surprisingly, with ordinary abiotic d作者: 帶傷害 時間: 2025-3-26 11:21
Hybrid Debugging of Java Programs data using primarily statistical criteria. Features are now selected or extracted that have the highest impact on the prediction of the response/target variable based on various statistical solution methods. This chapter describes methods using linear regression and regularization that afford solut作者: 牛馬之尿 時間: 2025-3-26 15:48
Hybrid Debugging of Java Programsictors and thus select or extract features that enable solutions to complex questions from large datasets. This chapter reviews various machine learning methods for dimensionality reduction, including autoencoders, neural networks themselves, and other methods.作者: 清唱劇 時間: 2025-3-26 20:10 作者: 陪審團每個人 時間: 2025-3-26 23:09 作者: transplantation 時間: 2025-3-27 01:18
Solutions to Data Science Problems,supervised and unsupervised algorithms are described along with practical considerations when using these methods. Empirical results on exemplar datasets are also presented where applicable to illustrate the application of these methods to real-world problems.作者: 豐滿有漂亮 時間: 2025-3-27 06:39 作者: NATAL 時間: 2025-3-27 10:07 作者: 多樣 時間: 2025-3-27 16:55 作者: AXIS 時間: 2025-3-27 20:41
http://image.papertrans.cn/e/image/280475.jpg作者: gentle 時間: 2025-3-27 22:18 作者: MERIT 時間: 2025-3-28 04:10 作者: Communal 時間: 2025-3-28 07:06 作者: BAN 時間: 2025-3-28 13:17 作者: 沙發(fā) 時間: 2025-3-28 16:20 作者: 連鎖,連串 時間: 2025-3-28 19:44
What Is Dimensionality Reduction (DR)?,ity to generate, gather, and store volumes of data (order of tera- and exo-bytes, 10.???10. daily) has far outpaced our ability to derive useful information from it in many fields, with available computational resources. Therefore, data reduction is a critical step in order to turn large datasets in作者: 爆炸 時間: 2025-3-29 00:58
Conventional Statistical Approaches,space. Statistical methods aim to preserve characteristic parameters such as mean, variance, and covariance of features in the population, as estimated from the dataset. Methods include Principal Component Analysis (PCA) and its variants, Independent component analysis and Discriminant Analysis. Lin作者: Apraxia 時間: 2025-3-29 06:08
Geometric Approaches,r of features. After the classical PCA that fits a linear (flat) subspace so that the total sum of squared distances of the data from the subspace (errors) is minimized, any distance function in this space can be used to endow it with a geometric structure, where ordinary intuition can be particular作者: Left-Atrium 時間: 2025-3-29 10:50 作者: febrile 時間: 2025-3-29 11:40
