標(biāo)題: Titlebook: Big Data Preprocessing; Enabling Smart Data Julián Luengo,Diego García-Gil,Francisco Herrera Book 2020 Springer Nature Switzerland AG 2020 [打印本頁] 作者: 萬圣節(jié) 時間: 2025-3-21 17:46
書目名稱Big Data Preprocessing影響因子(影響力)
書目名稱Big Data Preprocessing影響因子(影響力)學(xué)科排名
書目名稱Big Data Preprocessing網(wǎng)絡(luò)公開度
書目名稱Big Data Preprocessing網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Big Data Preprocessing被引頻次
書目名稱Big Data Preprocessing被引頻次學(xué)科排名
書目名稱Big Data Preprocessing年度引用
書目名稱Big Data Preprocessing年度引用學(xué)科排名
書目名稱Big Data Preprocessing讀者反饋
書目名稱Big Data Preprocessing讀者反饋學(xué)科排名
作者: SEVER 時間: 2025-3-21 21:54 作者: 松馳 時間: 2025-3-22 01:36 作者: Indict 時間: 2025-3-22 07:59 作者: bleach 時間: 2025-3-22 12:31 作者: 幻影 時間: 2025-3-22 16:05
https://doi.org/10.1007/978-1-4614-5987-3and science. However, because of the myriad of existing tools, it is often difficult for practitioners and experts to analyze and select the correct tool for their problems. In this chapter we present an introductory summary to the wide environment of Big Data with the aim of providing necessary kno作者: Engaged 時間: 2025-3-22 18:27 作者: STELL 時間: 2025-3-23 01:10
Comparative Analysis of Political Value,arises as a possible solution to enable large-scale learning with millions of dimensions. Nevertheless, as any other family of algorithms, reduction methods require an upgrade in its design so that they can work with such magnitudes. Particularly, they must be prepared to tackle the explosive combin作者: 尖牙 時間: 2025-3-23 01:53
Comparative Analysis of Political Cognition,pace and better define the decision boundaries between classes. Theoretically, reduction techniques should enable the application of learning algorithms on large-scale problems. Nevertheless, standard algorithms suffer from the increment on size and complexity of today’s problems. The objective of t作者: 赦免 時間: 2025-3-23 08:58 作者: ARCHE 時間: 2025-3-23 11:15 作者: 膽小鬼 時間: 2025-3-23 14:40 作者: 啪心兒跳動 時間: 2025-3-23 18:47
Introduction to Compiler Designramework that implemented the MapReduce paradigm. Apache Spark appeared a few years later improving the Hadoop Ecosystem. Similarly, Apache Flink appeared in the last years for tackling the Big Data streaming problem. However, as these frameworks were created for dealing with huge amounts of data, m作者: 橡子 時間: 2025-3-24 02:02
https://doi.org/10.1007/978-0-85729-829-4nowledge and insights we can extract from it. Referring to the well-known “garbage in, garbage out” principle, accumulating vast amounts of raw data will not guarantee quality results, but poor knowledge. In this last chapter we aim to provide a couple of final thoughts on the importance of data pre作者: 滑動 時間: 2025-3-24 05:23
Book 2020st relevant proposed solutions. This book illustrates actual implementations of algorithms that helps the reader deal with these problems.?.This book stresses the gap that exists between big, raw data and the requirements of quality data that businesses are demanding. This is called Smart Data, and 作者: Antagonism 時間: 2025-3-24 09:17
Introduction to Compiler Designitical impact in the learning process, as most learners suppose that the data is complete. However, in this Big Data era, the massive growth in the scale of the data poses a challenge to traditional proposals created to tackle noise and missing values, as they have difficulties coping with such a large amount of data.作者: follicle 時間: 2025-3-24 13:23
Introduction to Compiler Designthe early proposals on dealing with parallel discretization. Then, we present some distributed solutions capable of scaling on large-scale datasets. We finish with a study of the discretization methods capable of dealing with Big Data streams.作者: Phagocytes 時間: 2025-3-24 18:55 作者: Catheter 時間: 2025-3-24 20:21 作者: Tracheotomy 時間: 2025-3-25 01:33 作者: certitude 時間: 2025-3-25 04:01
Imbalanced Data Preprocessing for Big Data,this divide-and-conquer strategy entails several problems, such as small disjuncts, data lack, etc. In this chapter we also review the latest proposals on imbalanced Big Data preprocessing and present a MapReduce framework for imbalanced preprocessing which includes several state-of-the-art sampling techniques.作者: FIR 時間: 2025-3-25 08:42
https://doi.org/10.1007/978-1-4614-5987-3pular frameworks in Big Data, and their main components. Next we also discuss other novel platforms for high-speed streaming processing that are gaining increasing importance in industry. Finally we make a comparison between two of the most relevant large-scale processing platforms nowadays: Spark and Flink.作者: Noisome 時間: 2025-3-25 14:56 作者: 動機(jī) 時間: 2025-3-25 18:37 作者: 礦石 時間: 2025-3-25 22:03 作者: Compassionate 時間: 2025-3-26 01:03
of some of the most recent solutions to address imbalanced This book offers a comprehensible overview of? Big Data Preprocessing, which includes a formal description of each problem.? It also focuses on the most relevant proposed solutions. This book illustrates actual implementations of algorithms作者: brassy 時間: 2025-3-26 07:00 作者: 陳腐的人 時間: 2025-3-26 12:26 作者: 歌曲 時間: 2025-3-26 14:45
Book 2020e novel areas of study that are gathering a deeper attention on the Big Data preprocessing. Specifically, it considers the relation with Deep Learning (as of a technique that also relies in large volumes of data), the difficulty of finding the appropriate selection and concatenation of preprocessing作者: 收集 時間: 2025-3-26 20:02 作者: antecedence 時間: 2025-3-26 22:55
Smart Data,we give an insight of the state of Smart Data. Next, we provide a discussion on how to move from Big to Smart Data. We finish with an introduction to Smart Data and its relation with the Internet of Things.作者: 小說 時間: 2025-3-27 02:13
Dimensionality Reduction for Big Data, as a case study, we study in depth the design and behavior of one of the most popular selection frameworks in this field. Finally, we study all contributions related to dimensionality reduction in Big Data streams.作者: enflame 時間: 2025-3-27 08:57
Big Data Software,tion of Apache Spark MLlib and all of its components. We continue with a description of a Big Data library focused on data preprocessing for Apache Spark, named BigDaPSpark. Next, we provide an extensive analysis of FlinkML, and its included algorithms and utilities. Lastly, we finish with the descr作者: 潔凈 時間: 2025-3-27 13:31
. Specifically, it considers the relation with Deep Learning (as of a technique that also relies in large volumes of data), the difficulty of finding the appropriate selection and concatenation of preprocessing978-3-030-39107-2978-3-030-39105-8作者: 使殘廢 時間: 2025-3-27 14:32 作者: 救護(hù)車 時間: 2025-3-27 21:44 作者: grudging 時間: 2025-3-27 23:50
Comparative Analysis of Political Value, as a case study, we study in depth the design and behavior of one of the most popular selection frameworks in this field. Finally, we study all contributions related to dimensionality reduction in Big Data streams.作者: Laconic 時間: 2025-3-28 05:19 作者: GRIN 時間: 2025-3-28 06:57 作者: 指派 時間: 2025-3-28 13:23 作者: 怕失去錢 時間: 2025-3-28 18:23 作者: 管理員 時間: 2025-3-28 21:35
Dimensionality Reduction for Big Data,arises as a possible solution to enable large-scale learning with millions of dimensions. Nevertheless, as any other family of algorithms, reduction methods require an upgrade in its design so that they can work with such magnitudes. Particularly, they must be prepared to tackle the explosive combin作者: chemoprevention 時間: 2025-3-28 23:11
Data Reduction for Big Data,pace and better define the decision boundaries between classes. Theoretically, reduction techniques should enable the application of learning algorithms on large-scale problems. Nevertheless, standard algorithms suffer from the increment on size and complexity of today’s problems. The objective of t作者: BANAL 時間: 2025-3-29 03:35 作者: abreast 時間: 2025-3-29 10:29
Big Data Discretization, a wide range of machine learning methods. With the advent of Big Data, a new wave of large-scale datasets with predominance of continuous features have arrived to industry and academia. However, standard discretizers do not respond well to huge sets of continuous points, and novel distributed discr作者: NAVEN 時間: 2025-3-29 14:06 作者: CAB 時間: 2025-3-29 15:51 作者: 從容 時間: 2025-3-29 19:47
Final Thoughts: From Big Data to Smart Data,nowledge and insights we can extract from it. Referring to the well-known “garbage in, garbage out” principle, accumulating vast amounts of raw data will not guarantee quality results, but poor knowledge. In this last chapter we aim to provide a couple of final thoughts on the importance of data pre作者: 確認(rèn) 時間: 2025-3-30 00:00
Endometrial Cancer: Advanced Stage,ant treatment for advanced endometrial cancer. Hormonal therapy with variable response rates has been used for metastatic and recurrent endometrial carcinoma. The GOG continues to investigate multimodality therapy.
