標(biāo)題: Titlebook: Handbook of Big Data Analytics; Wolfgang Karl H?rdle,Henry Horng-Shing Lu,Xiaotong Book 2018 Springer International Publishing AG, part of [打印本頁] 作者: CULT 時(shí)間: 2025-3-21 17:51
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書目名稱Handbook of Big Data Analytics網(wǎng)絡(luò)公開度學(xué)科排名
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書目名稱Handbook of Big Data Analytics讀者反饋
書目名稱Handbook of Big Data Analytics讀者反饋學(xué)科排名
作者: 玩忽職守 時(shí)間: 2025-3-21 22:02
A. Tarnawski,H. Gergely,T. G. Douglass advanced analytics. CDA is especially important in the age of big data, where the data is so complex, and includes both structured and unstructured data, that it is impossible to manually examine all possible combinations. As a cognitive computing system, CDA does not simply take over the entire pr作者: surrogate 時(shí)間: 2025-3-22 01:34
https://doi.org/10.1007/978-3-658-25927-3cantly outpaces the increase of storage and computational capacity of high performance computers. The challenge in analyzing big data calls for innovative analytical and computational methods that make better use of currently available computing power. An emerging powerful family of methods for effe作者: ordain 時(shí)間: 2025-3-22 07:46
,Therapie der ?sophagusvarizenblutung,ence is pursued. Distributed statistical inference is a technique to tackle a type of the above problem, and has recently attracted enormous attention. Many existing work focus on the averaging estimator, e.g., Zhang et al. (2013) and many others. In this chapter, we propose a one-step approach to e作者: 技術(shù) 時(shí)間: 2025-3-22 11:54
https://doi.org/10.1007/978-3-662-10458-3ta. Traditional nonparametric methods are challenged by modern high dimensional data due to the curse of dimensionality. Over the past two decades, there have been rapid advances in nonparametrics to accommodate analysis of large-scale and high dimensional data. A variety of cutting-edge nonparametr作者: 繁忙 時(shí)間: 2025-3-22 13:35
Siegfried Kasper,Hans-Jürgen M?llers. A problem of current interest is clustering and classification of multiple time series. When various time series are fitted to models, the different time series can be grouped into clusters based on the fitted models. If there are different identifiable classes of time series, the fitted models c作者: 巨碩 時(shí)間: 2025-3-22 21:04 作者: 安心地散步 時(shí)間: 2025-3-23 00:42
Therapeutisches Arbeiten mit Tr?umendistributional approximations of functionals of non-Gaussian vectors by those of Gaussian ones. Differently from the widely used Bonferroni approach, our procedure is dependence-adjusted and has an asymptotically correct size and power. To obtain cutoff values of our test, we propose a half-sampling作者: 惡意 時(shí)間: 2025-3-23 04:11 作者: ANTH 時(shí)間: 2025-3-23 07:13 作者: accessory 時(shí)間: 2025-3-23 12:10 作者: 代理人 時(shí)間: 2025-3-23 16:37
Sympathikusblockaden in der Praxis,enges in modern data analysis. Most forward regression modeling procedures are seriously compromised due to the curse of dimension. In this chapter, we show that the inverse modeling idea, originated from the . (SIR), can help us detect nonlinear relations effectively, and survey a few recent advanc作者: enumaerate 時(shí)間: 2025-3-23 18:06
E. Specker,H. Gülker,F. Bender,A. Theilmeiersing, and Internet search. How to extract useful information from massive data becomes the key issue nowadays. In spite of the urgent need for statistical tools to deal with such data, there are limited methods that can fully address the high-dimensional problem. In this chapter, we review the gener作者: 極為憤怒 時(shí)間: 2025-3-24 02:08 作者: Customary 時(shí)間: 2025-3-24 06:06
J. J. Herzberg,H. Hilmer,K. Wulfreproducibility and offering a platform for sharing validated knowledge native to the social web. To increase the information retrieval (IR) efficiency there is a need for incorporating semantic information. Three text mining models will be examined: vector space model (VSM), generalized VSM (GVSM),作者: figment 時(shí)間: 2025-3-24 07:40 作者: gnarled 時(shí)間: 2025-3-24 13:06 作者: 向下五度才偏 時(shí)間: 2025-3-24 15:51
Statistical Leveraging Methods in Big Datacantly outpaces the increase of storage and computational capacity of high performance computers. The challenge in analyzing big data calls for innovative analytical and computational methods that make better use of currently available computing power. An emerging powerful family of methods for effe作者: 綠州 時(shí)間: 2025-3-24 22:18 作者: 裁決 時(shí)間: 2025-3-25 00:12
Nonparametric Methods for Big Data Analyticsta. Traditional nonparametric methods are challenged by modern high dimensional data due to the curse of dimensionality. Over the past two decades, there have been rapid advances in nonparametrics to accommodate analysis of large-scale and high dimensional data. A variety of cutting-edge nonparametr作者: exceed 時(shí)間: 2025-3-25 07:12 作者: neurologist 時(shí)間: 2025-3-25 10:13 作者: BLANC 時(shí)間: 2025-3-25 14:02 作者: pester 時(shí)間: 2025-3-25 19:43
High-Dimensional Classificationmputation. In the past 15 years, several popular high-dimensional classifiers have been developed and studied in the literature. These classifiers can be roughly divided into two categories: sparse penalized margin-based classifiers and sparse discriminant analysis. In this chapter we give a compreh作者: 替代品 時(shí)間: 2025-3-26 00:00
Analysis of High-Dimensional Regression Models Using Orthogonal Greedy AlgorithmsA) in high-dimensional sparse regression models with independent observations. In particular, when the regression coefficients are absolutely summable, the conditional mean squared prediction error and the empirical norm of OGA derived by Ing and Lai (Stat Sin 21:1473–1513, 2011) are introduced. We 作者: Inkling 時(shí)間: 2025-3-26 03:19 作者: 波動 時(shí)間: 2025-3-26 08:08
Inverse Modeling: A Strategy to Cope with Non-linearityenges in modern data analysis. Most forward regression modeling procedures are seriously compromised due to the curse of dimension. In this chapter, we show that the inverse modeling idea, originated from the . (SIR), can help us detect nonlinear relations effectively, and survey a few recent advanc作者: 極深 時(shí)間: 2025-3-26 10:02 作者: 雀斑 時(shí)間: 2025-3-26 15:08
Bridging Density Functional Theory and Big Data Analytics with Applicationsregime. By technically mapping the data space into physically meaningful bases, the chapter provides a simple procedure to formulate global Lagrangian and Hamiltonian density functionals to circumvent the emerging challenges on large-scale data analyses. Then, the informative features of mixed datas作者: 講個故事逗他 時(shí)間: 2025-3-26 19:12 作者: 煩人 時(shí)間: 2025-3-26 21:53 作者: 漸強(qiáng) 時(shí)間: 2025-3-27 01:33
978-3-030-13238-5Springer International Publishing AG, part of Springer Nature 2018作者: 載貨清單 時(shí)間: 2025-3-27 06:51 作者: 顧客 時(shí)間: 2025-3-27 09:26 作者: ablate 時(shí)間: 2025-3-27 14:11 作者: adjacent 時(shí)間: 2025-3-27 19:23
Gespr?chspsychotherapie der DepressionCompressive sensing is a technique to acquire signals at rates proportional to the amount of information in the signal, and it does so by exploiting the sparsity of signals. This section discusses the fundamentals of compressive sensing, and how it is related to sparse coding.作者: ASSET 時(shí)間: 2025-3-27 22:55 作者: calorie 時(shí)間: 2025-3-28 02:38
https://doi.org/10.1007/978-3-7985-1919-0this new area of investigation. At the same time, data practitioners will be exposed to the possibility of privacy breaches, accidents causing bodily harm, and other concrete consequences of getting things wrong in theory and/or practice. We contend that the physical instantiation of data practice i作者: 確定 時(shí)間: 2025-3-28 08:51 作者: 粗語 時(shí)間: 2025-3-28 11:07 作者: 終止 時(shí)間: 2025-3-28 16:50
,Therapie der ?sophagusvarizenblutung,, numerical examples show that the proposed estimator outperforms the simple averaging estimator with a large margin in terms of the mean squared errors. A potential application of the one-step approach is that one can use multiple machines to speed up large-scale statistical inference with little c作者: 食物 時(shí)間: 2025-3-28 19:26
Siegfried Kasper,Hans-Jürgen M?llers and to identify an adequate local model within each regime. In this case, the problem of clustering or classification can be addressed by use of sequential patterns of the models for the separate regimes..In this chapter, we discuss methods for identifying changepoints in a univariate time series.作者: Sputum 時(shí)間: 2025-3-28 22:57
E. Specker,H. Gülker,F. Bender,A. Theilmeier the number of samples. To preserve the tensor structure and reduce the dimensionality simultaneously, we revisit the tensor sufficient dimension reduction model and apply it to colorimetric sensor arrays. Tensor sufficient dimension reduction method is simple but powerful and exhibits a competent e作者: Slit-Lamp 時(shí)間: 2025-3-29 05:29
https://doi.org/10.1007/3-7985-1622-7 also applied on the post-process of magnetic resonance imaging (MRI) and better tumor recognitions can be achieved on the T1 post-contrast and T2 modes. It is appealing that the post-processing MRI using the proposed DFT-based algorithm would benefit the scientists in the judgment of clinical patho作者: Breach 時(shí)間: 2025-3-29 09:40 作者: BARGE 時(shí)間: 2025-3-29 14:12
Statistics, Statisticians, and the Internet of Thingsthis new area of investigation. At the same time, data practitioners will be exposed to the possibility of privacy breaches, accidents causing bodily harm, and other concrete consequences of getting things wrong in theory and/or practice. We contend that the physical instantiation of data practice i作者: 躲債 時(shí)間: 2025-3-29 16:24
Cognitive Data Analysis for Big Dataata Preparation, Automated Modeling, and Application of Results) is discussed in detail. The Data Preparation stage alleviates or eliminates the data preparation burden from the user by including smart technologies such as natural language query and metadata discovery. This stage prepares the data f作者: Popcorn 時(shí)間: 2025-3-29 21:45
Statistical Leveraging Methods in Big Datainference. In this chapter, we review the recent development of statistical leveraging methods. In particular, we focus on various algorithms for constructing subsampling probability distribution, and a coherent theoretical framework for investigating their estimation property and computing complexi作者: CEDE 時(shí)間: 2025-3-30 03:58
Scattered Data and Aggregated Inference, numerical examples show that the proposed estimator outperforms the simple averaging estimator with a large margin in terms of the mean squared errors. A potential application of the one-step approach is that one can use multiple machines to speed up large-scale statistical inference with little c作者: 烤架 時(shí)間: 2025-3-30 07:41
Finding Patterns in Time Seriess and to identify an adequate local model within each regime. In this case, the problem of clustering or classification can be addressed by use of sequential patterns of the models for the separate regimes..In this chapter, we discuss methods for identifying changepoints in a univariate time series.作者: Interregnum 時(shí)間: 2025-3-30 09:27
Sufficient Dimension Reduction for Tensor Data the number of samples. To preserve the tensor structure and reduce the dimensionality simultaneously, we revisit the tensor sufficient dimension reduction model and apply it to colorimetric sensor arrays. Tensor sufficient dimension reduction method is simple but powerful and exhibits a competent e作者: Cabinet 時(shí)間: 2025-3-30 13:23
Bridging Density Functional Theory and Big Data Analytics with Applications also applied on the post-process of magnetic resonance imaging (MRI) and better tumor recognitions can be achieved on the T1 post-contrast and T2 modes. It is appealing that the post-processing MRI using the proposed DFT-based algorithm would benefit the scientists in the judgment of clinical patho作者: Aggregate 時(shí)間: 2025-3-30 20:30
Q3-D3-LSA: D3.js and Generalized Vector Space Models for Statistical Computinger .. evaluation. QuantNet and the corresponding Data-Driven Documents (D3) based visualization can be found and applied under .. The driving technology behind it is Q3-D3-LSA, which is the combination of “GitHub API based QuantNet Mining infrastructure in .”, LSA and D3 implementation.作者: Pituitary-Gland 時(shí)間: 2025-3-30 23:20
https://doi.org/10.1007/978-3-662-10458-3ic methodologies, scalable algorithms, and the state-of-the-art computational tools have been designed for model estimation, variable selection, statistical inferences for high dimensional regression, and classification problems. This chapter provides an overview of recent advances on nonparametrics in big data analytics.作者: falsehood 時(shí)間: 2025-3-31 02:39 作者: 加入 時(shí)間: 2025-3-31 08:32
Book 2018or statisticians, computer experts, engineers and application developers interested in using big data analytics with statistics. Readers should have a solid background in statistics and computer science. ?作者: 重疊 時(shí)間: 2025-3-31 09:48 作者: 心胸開闊 時(shí)間: 2025-3-31 16:52
Therapie bakterieller Infektionene responses and the full feature data. This presentation is focused on local kernel regression methods in semi-supervised learning and provides a good starting point for understanding semi-supervised methods in general.作者: 膠狀 時(shí)間: 2025-3-31 18:50
Analysis of High-Dimensional Regression Models Using Orthogonal Greedy Algorithmsries models, and illustrate the advantage of our results compared to those established for Lasso by Basu and Michailidis (Ann Stat 43:1535–1567, 2015) and Wu and Wu (Electron J Stat 10:352–379, 2016).
