標(biāo)題: Titlebook: Big Data Analysis: New Algorithms for a New Society; Nathalie Japkowicz,Jerzy Stefanowski Book 2016 Springer International Publishing Swit [打印本頁(yè)] 作者: LANK 時(shí)間: 2025-3-21 16:40
書目名稱Big Data Analysis: New Algorithms for a New Society影響因子(影響力)
書目名稱Big Data Analysis: New Algorithms for a New Society影響因子(影響力)學(xué)科排名
書目名稱Big Data Analysis: New Algorithms for a New Society網(wǎng)絡(luò)公開度
書目名稱Big Data Analysis: New Algorithms for a New Society網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Big Data Analysis: New Algorithms for a New Society被引頻次
書目名稱Big Data Analysis: New Algorithms for a New Society被引頻次學(xué)科排名
書目名稱Big Data Analysis: New Algorithms for a New Society年度引用
書目名稱Big Data Analysis: New Algorithms for a New Society年度引用學(xué)科排名
書目名稱Big Data Analysis: New Algorithms for a New Society讀者反饋
書目名稱Big Data Analysis: New Algorithms for a New Society讀者反饋學(xué)科排名
作者: IST 時(shí)間: 2025-3-21 22:16
Book 2016e benefits brought upon by Big Data Analysis are underlined, the book also discusses some of the warnings that have been issued concerning the potential dangers of Big Data Analysis along with its pitfalls and challenges..作者: synovitis 時(shí)間: 2025-3-22 01:50 作者: synchronous 時(shí)間: 2025-3-22 04:51 作者: Recessive 時(shí)間: 2025-3-22 10:48 作者: avulsion 時(shí)間: 2025-3-22 13:55 作者: craven 時(shí)間: 2025-3-22 17:41 作者: Mri485 時(shí)間: 2025-3-22 22:56
Bart Depreitere,Geert Meyfroidt,Fabian Güizant features and rule networks do not only reflect some syntactical properties of the data and classifiers but also may convey meaningful clues about true interactions in the modeled biological system. In this chapter we develop further our method of Monte Carlo Feature Selection and Interdependency 作者: FEAS 時(shí)間: 2025-3-23 05:10
An Insight on Big Data Analytics,mind the long-term academic training and field experience of statisticians concerning reduction of dataset volumes, sampling in a more general setting, data depreciation and quality, model design and validation, visualisation, etc. We expect that combining the present approaches will give incentives作者: Minikin 時(shí)間: 2025-3-23 08:08 作者: 我吃花盤旋 時(shí)間: 2025-3-23 13:39
Data Mining in Finance: Current Advances and Future Challenges,as the Velocity (in terms of speed of arrival) and the Variety, in terms of the various types of data collected. This chapter focuses on techniques that address these issues. Specifically, we turn our attention to the financial sector, which has become paramount to business. Our discussion centers o作者: Prostaglandins 時(shí)間: 2025-3-23 14:16 作者: CHASE 時(shí)間: 2025-3-23 20:46 作者: Tdd526 時(shí)間: 2025-3-24 01:55
Discovering Networks of Interdependent Features in High-Dimensional Problems,nt features and rule networks do not only reflect some syntactical properties of the data and classifiers but also may convey meaningful clues about true interactions in the modeled biological system. In this chapter we develop further our method of Monte Carlo Feature Selection and Interdependency 作者: 跟隨 時(shí)間: 2025-3-24 02:20
Big Data Analysis: New Algorithms for a New Society作者: 尖牙 時(shí)間: 2025-3-24 06:41
J. Gasco,J. Sendra,J. Lim,I. Ngs within a spectrum of problems related to concept drift. Finally, we discuss some promising research directions from the application perspective, and present recommendations for application driven concept drift research and development.作者: 逃避現(xiàn)實(shí) 時(shí)間: 2025-3-24 10:53 作者: 豎琴 時(shí)間: 2025-3-24 18:27
An Overview of Concept Drift Applications,s within a spectrum of problems related to concept drift. Finally, we discuss some promising research directions from the application perspective, and present recommendations for application driven concept drift research and development.作者: 輪流 時(shí)間: 2025-3-24 20:56
Big Data and the Internet of Things,mprove” their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.作者: CHARM 時(shí)間: 2025-3-25 00:24
Book 2016ea. ..It demonstrates that Big Data Analysis opens up new research problems which were either never considered before, or were only considered within a limited range. In addition to providing methodological discussions on the principles of mining Big Data and the difference between traditional stati作者: Stable-Angina 時(shí)間: 2025-3-25 06:38
2197-6503 ithms and applications.Includes supplementary material: .This edited volume is devoted to Big Data Analysis from a Machine Learning standpoint as presented by some of the most eminent researchers in this area. ..It demonstrates that Big Data Analysis opens up new research problems which were either 作者: GRATE 時(shí)間: 2025-3-25 08:25
G. K. C. Wong,X. L. Zhu,W. S. Poonrk into separate homogeneous networks, followed by concatenating the structural context vectors calculated from separate homogeneous networks with the bag-of-words vectors obtained from textual information contained in certain network nodes. The approach is show-cased on the analysis of two real-life text-enriched heterogeneous citation networks.作者: PHON 時(shí)間: 2025-3-25 15:06 作者: 我們的面粉 時(shí)間: 2025-3-25 17:44
https://doi.org/10.1007/978-3-030-59436-7er presents the overall perspective for data analysis software for genomics and prospects for the emerging applications. To show genomic big data analysis in practice, a case study of the SparkSeq system that delivers tool for biological sequence analysis is presented.作者: debunk 時(shí)間: 2025-3-25 20:35
Analysis of Text-Enriched Heterogeneous Information Networks,rk into separate homogeneous networks, followed by concatenating the structural context vectors calculated from separate homogeneous networks with the bag-of-words vectors obtained from textual information contained in certain network nodes. The approach is show-cased on the analysis of two real-life text-enriched heterogeneous citation networks.作者: Painstaking 時(shí)間: 2025-3-26 03:19 作者: 煩躁的女人 時(shí)間: 2025-3-26 07:56 作者: 要塞 時(shí)間: 2025-3-26 10:18
A Machine Learning Perspective on Big Data Analysis,g, which is based on machine learning, reviews its achievements and discusses its impact on science and society. The chapter concludes with a summary of the book’s contributing chapters divided into problem-centric and domain-centric essays.作者: foppish 時(shí)間: 2025-3-26 15:50 作者: 僵硬 時(shí)間: 2025-3-26 20:31 作者: 向外 時(shí)間: 2025-3-26 22:27 作者: 一個(gè)姐姐 時(shí)間: 2025-3-27 04:52 作者: Hypopnea 時(shí)間: 2025-3-27 08:46
Implementing Big Data Analytics Projects in Business,derstand the value offered by Big Data and the processes needed to extract it. This chapter discusses why companies should progressively increase their data volumes and the process to follow for implementing a Big Data project. We present a variety of architectures, from in-memory servers to ., to h作者: 智力高 時(shí)間: 2025-3-27 11:01
Data Mining in Finance: Current Advances and Future Challenges,ing to rely on guesswork and incorrect extrapolations. Data mining algorithms equip institutions to predict the movements of financial indicators, enable companies to move towards more energy-efficient buildings, as well as allow businesses to conduct targeted marketing campaigns and forecast sales.作者: circumvent 時(shí)間: 2025-3-27 17:21
Industrial-Scale Ad Hoc Risk Analytics Using MapReduce,ensure capital adequacy and for fine-grained financial planning, these companies carry out large-scale Monte Carlo simulations to estimate the probabilities that the losses incurred due to catastrophic events such as hurricanes, earthquakes, etc. exceed certain critical values. This is a computation作者: Banquet 時(shí)間: 2025-3-27 19:36 作者: 滔滔不絕地說 時(shí)間: 2025-3-28 00:27
Social Network Analysis in Streaming Call Graphs,ction of social interactions between individuals, representing social structures. Call graphs can be deduced from these CDRs, where nodes represent subscribers and edges represent the phone calls made. These graphs may easily reach millions of nodes and billions of edges. Besides being large-scale a作者: Esophagitis 時(shí)間: 2025-3-28 02:52
Scalable Cloud-Based Data Analysis Software Systems for Big Data from Next Generation Sequencing,ds of machines worldwide produce daily billions of sequenced nucleotide base pairs of data. Due to continuous development of faster and economical sequencing technologies, processing the large amounts of data produced by high throughput sequencing technologies became the main challenge in bioinforma作者: anesthesia 時(shí)間: 2025-3-28 07:05
Discovering Networks of Interdependent Features in High-Dimensional Problems, various forms of sequencing has created both challenges in analyzing these data as well as new opportunities. A promising, yet underdeveloped approach to Big Data, not limited to Life Sciences, is the use of feature selection and classification to discover interdependent features. Traditionally, cl作者: inclusive 時(shí)間: 2025-3-28 14:17 作者: Uncultured 時(shí)間: 2025-3-28 16:45
Zofia H. Czosnyka,M. Czosnyka,J. D. Pickardg, which is based on machine learning, reviews its achievements and discusses its impact on science and society. The chapter concludes with a summary of the book’s contributing chapters divided into problem-centric and domain-centric essays.作者: LAPSE 時(shí)間: 2025-3-28 20:13 作者: GLUE 時(shí)間: 2025-3-29 02:42 作者: 熱心 時(shí)間: 2025-3-29 04:11
J. Gasco,J. Sendra,J. Lim,I. Ngn evolve over time, thus, models built for analyzing such data quickly become obsolete over time. In machine learning and data mining this phenomenon is referred to as concept drift. The objective is to deploy models that would diagnose themselves and adapt to changing data over time. This chapter p作者: clarify 時(shí)間: 2025-3-29 09:05
G. K. C. Wong,X. L. Zhu,W. S. Poon After an overview of tasks and approaches to mining heterogeneous information networks, the presentation focuses on text-enriched heterogeneous information networks whose distinguishing property is that certain nodes are enriched with text information. A particular approach to mining text-enriched 作者: Odyssey 時(shí)間: 2025-3-29 15:21
Time Constant of the Cerebral Arterial Bed,derstand the value offered by Big Data and the processes needed to extract it. This chapter discusses why companies should progressively increase their data volumes and the process to follow for implementing a Big Data project. We present a variety of architectures, from in-memory servers to ., to h作者: Coterminous 時(shí)間: 2025-3-29 18:58
J. K. Rhodes,S. Chandrasekaran,P. J. Andrewsing to rely on guesswork and incorrect extrapolations. Data mining algorithms equip institutions to predict the movements of financial indicators, enable companies to move towards more energy-efficient buildings, as well as allow businesses to conduct targeted marketing campaigns and forecast sales.作者: Mortal 時(shí)間: 2025-3-29 21:21 作者: 漂白 時(shí)間: 2025-3-30 01:25
Yong Bai,Daby Sow,Paul Vespa,Xiao Hung devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this results in an ecosystem of highly interconnected devices referred to as the Internet of Things—IoT. In conjunction, the advances in 作者: 全國(guó)性 時(shí)間: 2025-3-30 06:16
Bart Depreitere,Geert Meyfroidt,Fabian Güizaction of social interactions between individuals, representing social structures. Call graphs can be deduced from these CDRs, where nodes represent subscribers and edges represent the phone calls made. These graphs may easily reach millions of nodes and billions of edges. Besides being large-scale a作者: ACRID 時(shí)間: 2025-3-30 09:52
https://doi.org/10.1007/978-3-030-59436-7ds of machines worldwide produce daily billions of sequenced nucleotide base pairs of data. Due to continuous development of faster and economical sequencing technologies, processing the large amounts of data produced by high throughput sequencing technologies became the main challenge in bioinforma
