標(biāo)題: Titlebook: Mathematical Problems in Data Science; Theoretical and Prac Li M. Chen,Zhixun Su,Bo Jiang Book 2015 Springer International Publishing Switz [打印本頁] 作者: 自由才謹(jǐn)慎 時間: 2025-3-21 19:30
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作者: Brochure 時間: 2025-3-21 23:26
Introduction: Data Science and BigData Computinga. Today, we are supposed to find rules and properties in the data set, even among different data sets. In this chapter, we will explain data science and its relationship to BigData, cloud computing and data mining. We also discuss current research problems in data science and provide concerns relating to a baseline of the data science industry.作者: 胖人手藝好 時間: 2025-3-22 03:24 作者: 激怒某人 時間: 2025-3-22 07:10
Li M. Chen,Zhixun Su,Bo JiangExplains the most current methods for solving cutting edge problems in data science and big data.Provides problem solving techniques and case studies.Covers a wide range of mathematical problems in da作者: 宴會 時間: 2025-3-22 11:26 作者: 薄荷醇 時間: 2025-3-22 15:56 作者: installment 時間: 2025-3-22 17:13 作者: 神圣在玷污 時間: 2025-3-22 21:51
Monte Carlo Methods and Their Applications in Big Data Analysis estimation of sum, Monte Carlo linear solver, image recovery, matrix multiplication, and low-rank approximation are shown as case studies to demonstrate the effectiveness of Monte Carlo methods in data analysis.作者: 提名 時間: 2025-3-23 01:49 作者: 我要沮喪 時間: 2025-3-23 06:02 作者: 注意到 時間: 2025-3-23 13:19
Curve Interpolation and Financial Curve Constructionlied to the interest rate curve construction, this interpolation algorithm ensures positive values. The market data has been reconstructed to restrict the fluctuation of the interest rate curve when the market data changes sharply.作者: Anemia 時間: 2025-3-23 17:53 作者: 拖債 時間: 2025-3-23 21:19 作者: 變量 時間: 2025-3-23 23:49 作者: 小卷發(fā) 時間: 2025-3-24 04:48
five gene fusion detection tools, three mainly intended for RNA samples (EricScript, Arriba, FusionCatcher) and two for DNA samples (INTEGRATE and GeneFuse). The workflow runs on servers exploiting Nextflow (a DSL for data-driven computational pipelines), Docker containers, and Conda virtual enviro作者: 警告 時間: 2025-3-24 08:06 作者: 起波瀾 時間: 2025-3-24 14:07 作者: IRS 時間: 2025-3-24 16:26 作者: Estrogen 時間: 2025-3-24 21:02
Li M. Chenng the stored models. We evaluate the effectiveness of our postdiction pipeline in terms of storage reduction and data recovery accuracy using a real healthcare dataset. Our preliminary results show that the order in which outlier detection, clustering, and machine learning methods are applied leads作者: Comedienne 時間: 2025-3-25 03:14 作者: venous-leak 時間: 2025-3-25 06:22 作者: 廢除 時間: 2025-3-25 09:23 作者: shrill 時間: 2025-3-25 14:31 作者: Granular 時間: 2025-3-25 16:47 作者: Champion 時間: 2025-3-25 23:04
Pengfei Huang,Haiyan Wang,Ping Wu,Yifei Lilogies for designing high quality information systems, and new research and technological developments which use ontologies all over the life cycle of information systems..The 1.st. International Workshop on Technologies for Quality Management in Challenging Applications (TQMCA’2014) focuses on qual作者: NUL 時間: 2025-3-26 02:45
Jack Spencer,Ke Chenigh level (and qualitative) description of past and current situations defined over streams of medical data, complemented by projections into the future. Our proposed database extension allows for a compact and intuitive representation of medical data; much like physicians use abstraction from detai作者: companion 時間: 2025-3-26 07:45 作者: EPT 時間: 2025-3-26 12:09
Binhai Zhus both but is focused on OLAP or XML databases. In this paper, we describe a mechanism for storing concurrent versions of data in an OLTP database. We explore two different implementation alternatives for versioned data storage and evaluate their relative merits given different workloads. We provide作者: 泥土謙卑 時間: 2025-3-26 13:15 作者: membrane 時間: 2025-3-26 19:35 作者: RALES 時間: 2025-3-26 22:26
Machine Learning for Data Science: Mathematical or Computationalal data sets. When more data samples are available, the algorithm must be able to adjust accordingly. Therefore, in cloud computing, and BigData related methods in data science, machine learning becomes the primary technology. We have introduced the PCA, .-NN and .-means, and other methods in artifi作者: 返老還童 時間: 2025-3-27 04:24
Images, Videos, and BigDatansider massive data processing. For instance, automated driving is a challenge to data science..In BigData related image processing, we will discuss the following topics in this chapter: (1) An overview of image and video segmentation, (2) Data storage and fast image segmentation, (3) Feature extrac作者: puzzle 時間: 2025-3-27 08:41 作者: travail 時間: 2025-3-27 12:52
A New Computational Model of Bigdata between the slave nodes is considered impossible or too costly, and (3) an extra slave processor, together with the data it carries, can be easily integrated into the system to support scalability. Under such a model capturing the most important characteristics of a practical MapReduce system, some作者: 有權(quán)威 時間: 2025-3-27 16:37
Book 2015ree parts.? The first part explores the fundamental tools of data science. It includes basic graph theoretical methods, statistical and AI methods for massive data sets. In second part, chapters focus on the procedural treatment of data science problems including machine learning methods, mathematic作者: tympanometry 時間: 2025-3-27 21:39
Li M. Chenals: 2nd Workshop on Knowledge Graphs Analysis on?a Large Scale,?MADEISD: 5th Workshop on Modern Approaches in Data?Engineering, Information System Design,?PeRS978-3-031-42940-8978-3-031-42941-5Series ISSN 1865-0929 Series E-ISSN 1865-0937 作者: 含沙射影 時間: 2025-3-28 00:23
ic graph theoretical methods, statistical and AI methods for massive data sets. In second part, chapters focus on the procedural treatment of data science problems including machine learning methods, mathematic978-3-319-79739-7978-3-319-25127-1作者: Limpid 時間: 2025-3-28 02:46 作者: Exploit 時間: 2025-3-28 09:04
Overview of Basic Methods for Data Science graph search algorithms, statistical methods especially principal component analysis (PCA), algorithms and data structures, and data mining and pattern recognition. This chapter will provide an overview for machine learning in relation to other mathematical tools. We will first introduce graphs and作者: ARC 時間: 2025-3-28 12:03 作者: Incorporate 時間: 2025-3-28 17:23
Machine Learning for Data Science: Mathematical or Computationalet is used to verify the model. In general, a machine learning method requires an iterated process for reaching a goal. Machine learning is one of the research areas in artificial intelligence. Machine learning is mainly used to solve problems in classification and clustering. The distinction is tha作者: 明智的人 時間: 2025-3-28 20:42
Images, Videos, and BigData% of their storage. Therefore, data processing related to images is an essential topic in data science. The tasks concerning images and videos are mainly object search, recognition, and tracking. Current and future applications of images and videos include security and surveillance, medical imaging,作者: DECRY 時間: 2025-3-29 02:33
Topological Data Analysis?Modern data science also uses topological methods to find the structural features of data sets. In fact, topological methods should be the first step before the classification method is applied in most cases. Persistent homology is the most successful method for finding the topological structure of作者: 駕駛 時間: 2025-3-29 07:02
Monte Carlo Methods and Their Applications in Big Data Analysisfirst review the fundamental principles of Monte Carlo methods. Then, we describe several popular variance reduction techniques, including stratified sampling, control variates, antithetic variates, and importance sampling, to improve Monte Carlo sampling efficiency. Finally, application examples of作者: 生銹 時間: 2025-3-29 07:54
Feature Extraction via Vector Bundle Learningidered as the intrinsic structure to extract features from high dimensional data. By defining a manifold to model the structure of sample set, features sampled from each fibre of a vector bundle can be obtained by metric learning on the manifold. A number of existing algorithms can be reformulated a作者: 蕁麻 時間: 2025-3-29 14:11 作者: Calculus 時間: 2025-3-29 17:19 作者: 設(shè)施 時間: 2025-3-29 21:05
An On-Line Strategy of Groups Evacuation from a Convex Region in the Plane information completely. We seek strategies that achieve a bounded ratio of evacuation path length, without any knowledge of boundary information. We restrict the affected area to a convex region in the plane, and present a .-competitive strategy for . groups of evacuees (.?≥?3). The performance of 作者: 六邊形 時間: 2025-3-30 02:52
A New Computational Model of Bigdatat be done with such a model? These are questions demanding answers. Recently, a model was proposed to address this issue by simulating a restricted version of the PRAM model. In this paper, we propose a theoretical model called Master/Slave Multiprocessor (MSM for short) which is very similar to a p作者: 雕鏤 時間: 2025-3-30 06:41
c. In Brazil, one of the primary sources of misinformation is the messaging application WhatsApp. Thus, the automatic misinformation detection (MID) about COVID-19 in Brazilian Portuguese WhatsApp messages becomes a crucial challenge. Recently, some works presented different MID approaches for this 作者: 大猩猩 時間: 2025-3-30 11:38
nt and progression. However, identifying gene fusions is not a trivial process as it requires the management and processing countless amounts of data. Genomic data (particularly DNA and RNA) can reach up to 300 GB per sample. Furthermore, specific software and hardware architectures are required to
