標(biāo)題: Titlebook: Learning from Data Streams in Evolving Environments; Methods and Applicat Moamar Sayed-Mouchaweh Book 2019 Springer International Publishin [打印本頁] 作者: frustrate 時(shí)間: 2025-3-21 16:32
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書目名稱Learning from Data Streams in Evolving Environments影響因子(影響力)學(xué)科排名
書目名稱Learning from Data Streams in Evolving Environments網(wǎng)絡(luò)公開度
書目名稱Learning from Data Streams in Evolving Environments網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Learning from Data Streams in Evolving Environments被引頻次
書目名稱Learning from Data Streams in Evolving Environments被引頻次學(xué)科排名
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書目名稱Learning from Data Streams in Evolving Environments年度引用學(xué)科排名
書目名稱Learning from Data Streams in Evolving Environments讀者反饋
書目名稱Learning from Data Streams in Evolving Environments讀者反饋學(xué)科排名
作者: 舊石器時(shí)代 時(shí)間: 2025-3-21 21:23
Transfer Learning in Non-stationary Environments,line transfer learning in non-stationary environments. A brief summary of the results achieved by these approaches in the literature is presented, highlighting the benefits of integrating these two fields. As the first work to provide a detailed discussion of the relationship between transfer learni作者: animated 時(shí)間: 2025-3-22 04:28 作者: 碎石 時(shí)間: 2025-3-22 07:07
Error-Bounded Approximation of Data Stream: Methods and Theories,near-time algorithms are introduced to construct error-bounded piecewise linear representation for data stream. One generates the line segments by data convex analysis, and the other one is based on the transformed space, which can be extended to a general model. We theoretically analyzed and compar作者: Acquired 時(shí)間: 2025-3-22 11:58 作者: tenuous 時(shí)間: 2025-3-22 13:54 作者: 財(cái)主 時(shí)間: 2025-3-22 17:26 作者: ostensible 時(shí)間: 2025-3-22 22:38
Efficient Estimation of Dynamic Density Functions with Applications in Data Streams,timating the Probability Density Function (PDF) of the stream at a set of resampling points. KDE-Track is shown to be more accurate (as reflected by smaller error values) and more computationally efficient (as reflected by shorter running time) when compared with existing density estimation techniqu作者: Lymphocyte 時(shí)間: 2025-3-23 04:33 作者: Firefly 時(shí)間: 2025-3-23 09:20 作者: 座右銘 時(shí)間: 2025-3-23 12:13
Imen Khamassi,Moamar Sayed-Mouchaweh,Moez Hammami,Khaled Ghédira作者: 復(fù)習(xí) 時(shí)間: 2025-3-23 17:40 作者: 死亡 時(shí)間: 2025-3-23 20:24 作者: ethereal 時(shí)間: 2025-3-23 23:12
Fabíola S. F. Pereira,Shazia Tabassum,Jo?o Gama,Sandra de Amo,Gina M. B. Oliveira作者: 動(dòng)機(jī) 時(shí)間: 2025-3-24 06:26
Nicolas Kourtellis,Gianmarco De Francisci Morales,Albert Bifet作者: 撫育 時(shí)間: 2025-3-24 08:49
Book 2019ary environments;.Presents several application cases to show how the methods solve different real world problems;.Discusses the links between methods to help stimulate new research and application directions...作者: 羅盤 時(shí)間: 2025-3-24 12:22
Isah Abdullahi Lawalilities will most likely manifest themselves through a ., and may affect the overall materials’ resistance to deformation (flow stress). This size effect is distinguished from the length scales in plasticity, and .. For a few interacting crystals, the proposed model of slip-induced crystal plasticit作者: 老人病學(xué) 時(shí)間: 2025-3-24 18:08 作者: 不能逃避 時(shí)間: 2025-3-24 21:15
2197-6503 show how the methods solve different real world problems.Di.This edited book covers recent advances of techniques, methods and tools treating the problem of learning from data streams generated by evolving non-stationary processes. The goal is to discuss and overview the advanced techniques, method作者: 即席 時(shí)間: 2025-3-25 01:33
Book 2019-stationary processes. The goal is to discuss and overview the advanced techniques, methods and tools that are dedicated to manage, exploit and interpret data streams in non-stationary environments. The book includes the required notions, definitions, and background to understand the problem of lear作者: 簡略 時(shí)間: 2025-3-25 03:21 作者: outskirts 時(shí)間: 2025-3-25 08:46
Large-Scale Learning from Data Streams with Apache SAMOA,nd regression, as well as programming abstractions to develop new algorithms. It features a pluggable architecture that allows it to run on several distributed stream processing engines such as Apache Flink, Apache Storm, and Apache Samza. Apache SAMOA is written in Java and is available at . under the Apache Software License version 2.0.作者: 阻礙 時(shí)間: 2025-3-25 13:50 作者: 調(diào)整校對(duì) 時(shí)間: 2025-3-25 16:11
Analyzing and Clustering Pareto-Optimal Objects in Data Streams,uires new algorithms and methods to be able to learn under the evolving and unbounded data. In this chapter we focus on the task of .. We show that this method is a real alternative to the state-of-the-art approaches.作者: acheon 時(shí)間: 2025-3-25 23:31 作者: 比賽用背帶 時(shí)間: 2025-3-26 04:05
Transfer Learning in Non-stationary Environments,that come from different probability distributions. However, these two fields have evolved separately. Transfer learning enables knowledge to be transferred between different domains or tasks in order to improve predictive performance in a target domain and task. It has no notion of continuing time.作者: 妨礙 時(shí)間: 2025-3-26 04:28
A New Combination of Diversity Techniques in Ensemble Classifiers for Handling Complex Concept Driflly in dynamic environments, data are presented as streams that may evolve over time and this is known by .. Handling concept drift through ensemble classifiers has received a great interest in last decades. The success of these ensemble methods relies on their .. Accordingly, various diversity tech作者: Palate 時(shí)間: 2025-3-26 08:40
Analyzing and Clustering Pareto-Optimal Objects in Data Streams,der to learn from this ever-growing amount of data. Although many approaches exist for effective processing of data streams, learning from streams requires new algorithms and methods to be able to learn under the evolving and unbounded data. In this chapter we focus on the task of .. We show that th作者: COKE 時(shí)間: 2025-3-26 14:06
Error-Bounded Approximation of Data Stream: Methods and Theories,ention recently. To efficiently process and explore data streams, the compact data representation is playing an important role, since the data approximations other than the original data items are usually applied in many stream mining tasks, such as clustering, classification, and correlation analys作者: 捏造 時(shí)間: 2025-3-26 18:51
Ensemble Dynamics in Non-stationary Data Stream Classification,ost cases, can be read only once by the data mining algorithm. One of the most challenging problems in this process is how to learn such models in non-stationary environments, where the data/class distribution evolves over time. This phenomenon is called .. Ensemble learning techniques have been pro作者: 軌道 時(shí)間: 2025-3-26 23:40
Processing Evolving Social Networks for Change Detection Based on Centrality Measures,ther as time flies. The analysis of such networks is especially challenging, because it needs to be performed with an online approach, under the one-pass constraint of data streams. Such evolving behavior leads to changes in the network topology that can be investigated under different perspectives.作者: Permanent 時(shí)間: 2025-3-27 03:16 作者: craven 時(shí)間: 2025-3-27 09:07
Process Mining for Analyzing Customer Relationship Management Systems: A Case Study,building models that can detect patterns and behaviors. In the meanwhile, organizational perspective is being considered in Process Mining by taking advantage of the ability to extract social networks that represent different kinds of relations between resources performing the process. The case stud作者: forestry 時(shí)間: 2025-3-27 11:35 作者: 仔細(xì)閱讀 時(shí)間: 2025-3-27 14:04 作者: 不能根除 時(shí)間: 2025-3-27 20:03 作者: 名詞 時(shí)間: 2025-3-27 23:11
On Social Network-Based Algorithms for Data Stream Clustering,rs for many decades. This task becomes even more problematic when data is presented as a potentially unbounded sequence, the so-called data streams. Albeit most of the research on data stream mining focuses on supervised learning, the assumption that labels are available for learning is unverifiable作者: CHAR 時(shí)間: 2025-3-28 04:11
Isah Abdullahi Lawaltal data on various metals over broad ranges of strain rates, from quasi-static to 10./s and greater, and temperatures from 77 to 1,300K and greater. In this model, the role of the strain gradient is embedded in the nature of the dislocations, their density and distribution, and the manner by which 作者: 膠狀 時(shí)間: 2025-3-28 07:15 作者: drusen 時(shí)間: 2025-3-28 11:34
978-3-030-07862-1Springer International Publishing AG, part of Springer Nature 2019作者: Inculcate 時(shí)間: 2025-3-28 18:04
Learning from Data Streams in Evolving Environments978-3-319-89803-2Series ISSN 2197-6503 Series E-ISSN 2197-6511 作者: cortisol 時(shí)間: 2025-3-28 21:34
Incremental SVM Learning: Review,assification in evolving environments. We formalize a taxonomy of these methods based on their characteristics and the type of solution they provide. We discuss the strength and weakness of the various learning methods and also highlight some applications involving data stream, where incremental SVM learning has been used.作者: Tempor 時(shí)間: 2025-3-29 02:17
Moamar Sayed-MouchawehProvides multiple examples to facilitate the understanding data streams in non-stationary environments.Presents several application cases to show how the methods solve different real world problems.Di作者: Indolent 時(shí)間: 2025-3-29 06:28 作者: conception 時(shí)間: 2025-3-29 09:41 作者: HILAR 時(shí)間: 2025-3-29 13:18
Chatwara Suwannamai Duranages, and on the other hand two distinct kinds of phenomena, first observed by Crabbe and Ekman, showing that the Tennant-Prawitz criterion for paradoxicality overgenerates. This volume is of interest to schola978-3-031-46923-7978-3-031-46921-3Series ISSN 1572-6126 Series E-ISSN 2212-7313 作者: exceptional 時(shí)間: 2025-3-29 15:34
Qin ZhangAn all-in-one resource for reference-level knowledge related to smart agriculture technologies.Suitable for anyone interested in the latest advancement in agriculture engineering and cutting edge tech作者: inspired 時(shí)間: 2025-3-29 22:14 作者: COST 時(shí)間: 2025-3-30 02:04
