標(biāo)題: Titlebook: Data Mining: Foundations and Intelligent Paradigms; VOLUME 2: Statistica Dawn E. Holmes,Lakhmi C. Jain Book 2012 Springer-Verlag Berlin Hei [打印本頁] 作者: trace-mineral 時(shí)間: 2025-3-21 18:38
書目名稱Data Mining: Foundations and Intelligent Paradigms影響因子(影響力)
書目名稱Data Mining: Foundations and Intelligent Paradigms影響因子(影響力)學(xué)科排名
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書目名稱Data Mining: Foundations and Intelligent Paradigms被引頻次
書目名稱Data Mining: Foundations and Intelligent Paradigms被引頻次學(xué)科排名
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書目名稱Data Mining: Foundations and Intelligent Paradigms讀者反饋
書目名稱Data Mining: Foundations and Intelligent Paradigms讀者反饋學(xué)科排名
作者: frivolous 時(shí)間: 2025-3-21 22:56
Anne Hemkendreis,Anna-Sophie Jürgenstaining just one binary search tree is not enough, we also need other data structures. Moreover, in order to guarantee equal discretization results, an up-to-date discretization cannot always be kept available, but we need to delay the updates to happen at periodic intervals. We also provide a comparative evaluation of the proposed algorithm.作者: corpuscle 時(shí)間: 2025-3-22 04:20 作者: 航海太平洋 時(shí)間: 2025-3-22 04:45
A New Approach and Its Applications for Time Series Analysis and Prediction Based on Moving Average margins, in order to establish a way to predict the next series term based on both, the original data set and a negligible error. The algorithm performances are evaluated using measurement data sets on monthly average Sunspot Number, Earthquakes and Pseudo-Periodical Synthetic Time Series.作者: Affectation 時(shí)間: 2025-3-22 11:53
1868-4394 ian Analysis” we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field..978-3-642-43429-7978-3-642-23241-1Series ISSN 1868-4394 Series E-ISSN 1868-4408 作者: 親密 時(shí)間: 2025-3-22 13:09
Book 2012 Intelligent Paradigms: Volume 2: Core Topics including Statistical, Time-Series and Bayesian Analysis” we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field..作者: 親密 時(shí)間: 2025-3-22 17:16 作者: nominal 時(shí)間: 2025-3-23 00:36 作者: Accolade 時(shí)間: 2025-3-23 01:57 作者: CUB 時(shí)間: 2025-3-23 06:33 作者: 容易懂得 時(shí)間: 2025-3-23 10:24 作者: Reverie 時(shí)間: 2025-3-23 16:03
Advanced Modelling Paradigms in Data Mining, information. Acquiring and maintaing these repositories relies on mainstream techniques, technology and methodologies. In this book we discuss a number of founding techniques and expand into intelligent paradigms.作者: 減少 時(shí)間: 2025-3-23 18:50 作者: 火車車輪 時(shí)間: 2025-3-23 22:23
A Visual Environment for Designing and Running Data Mining Workflows in the Knowledge Grid,Knowledge Grid, a service-oriented framework for distributed data mining on the Grid. DIS3GNO supports all the phases of a distributed data mining task, including composition, execution, and results visualization. The paper provides a description of DIS3GNO, some relevant use cases implemented by it, and a performance evaluation of the system.作者: maculated 時(shí)間: 2025-3-24 05:12 作者: 頌揚(yáng)本人 時(shí)間: 2025-3-24 07:15
Exceptional Model Mining,omehow exceptional. We discuss regression as well as classification models, and define quality measures that determine how exceptional a given model on a subgroup is. Our framework is general enough to be applied to many types of models, even from other paradigms such as association analysis and graphical modeling.作者: OMIT 時(shí)間: 2025-3-24 13:25
1868-4394 th informatics is presented in a handbook style.Written by l.There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled “DATA MINING: Foundations and Intelligent Paradigms: Volume 2: Core Topics including Statistical, Time-Series and Bayes作者: 高度 時(shí)間: 2025-3-24 15:01 作者: 把手 時(shí)間: 2025-3-24 22:02
Sudeshna Roy,Ibrahim Seaga Shaws are the most popular neural network type, consisting on a feedforward network of processing neurons that are grouped into layers and connected by weighted links. On the other hand, SVM transforms the input variables into a high dimensional feature space and then finds the best hyperplane that mode作者: ingrate 時(shí)間: 2025-3-25 01:51 作者: 妨礙 時(shí)間: 2025-3-25 05:59 作者: Mosaic 時(shí)間: 2025-3-25 08:33
Natalia Chaban,Svetlana Beltyukovarules, leading to many technical improvments on the algorithms, and many different measures. But few number of them have tried to merge the both. We introduce here a formal framework for the study of association rules and interestingness measures that allows an analytic study of these objects. This 作者: Ornithologist 時(shí)間: 2025-3-25 12:07 作者: 組裝 時(shí)間: 2025-3-25 17:08 作者: 過時(shí) 時(shí)間: 2025-3-25 20:33
https://doi.org/10.1007/978-3-031-39787-5th the aim that, using mathematics, statistics and artificial intelligence methods, to analyze, process and make a prediction on the next most probable value based on a number of previous values. We propose an algorithm using the average sum of .. -order difference of series terms with limited range作者: diathermy 時(shí)間: 2025-3-26 01:00
Anne Hemkendreis,Anna-Sophie Jürgensase as a whole. In classical subgroup discovery, one considers the distribution of a single nominal attribute, and exceptional subgroups show a surprising increase in the occurrence of one of its values. In this paper, we describe . (EMM), a framework that allows for more complicated target concepts作者: acclimate 時(shí)間: 2025-3-26 05:53
Anne Hemkendreis,Anna-Sophie Jürgensits include that it is efficient, statistically justified, robust to noise, can be made to produce low-arity partitions, and has empirically been observed to work well in practice..The worst-case time requirement of the batch version of . bottom-up interval merging is . per attribute. We show that .作者: 厚顏無恥 時(shí)間: 2025-3-26 10:15 作者: 幼兒 時(shí)間: 2025-3-26 14:54
Data Mining: Foundations and Intelligent Paradigms978-3-642-23241-1Series ISSN 1868-4394 Series E-ISSN 1868-4408 作者: PRISE 時(shí)間: 2025-3-26 18:54
https://doi.org/10.1007/978-3-642-23241-1Computational Intelligence; Data Mining; Health Informatics; Intelligent Systems作者: 許可 時(shí)間: 2025-3-27 01:00 作者: detach 時(shí)間: 2025-3-27 04:44 作者: 溫室 時(shí)間: 2025-3-27 09:22 作者: 下垂 時(shí)間: 2025-3-27 12:33
Book 2012 Intelligent Paradigms: Volume 2: Core Topics including Statistical, Time-Series and Bayesian Analysis” we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field..作者: 傳授知識 時(shí)間: 2025-3-27 15:45
Data Mining: Foundations and Intelligent ParadigmsVOLUME 2: Statistica作者: APNEA 時(shí)間: 2025-3-27 19:22
,Regulatory Networks under Ellipsoidal Uncertainty – Data Analysis and Prediction by Optimization Thression problem and achieve a further relaxation by means of continuous optimization. We analyze the structure of the optimization problems obtained, especially, in view of their solvability, we discuss the structural frontiers and research challenges, and we conclude with an outlook.作者: 綠州 時(shí)間: 2025-3-28 00:40 作者: 譏笑 時(shí)間: 2025-3-28 02:39 作者: 思想上升 時(shí)間: 2025-3-28 08:47 作者: organic-matrix 時(shí)間: 2025-3-28 13:44
https://doi.org/10.1057/9781137499264ression problem and achieve a further relaxation by means of continuous optimization. We analyze the structure of the optimization problems obtained, especially, in view of their solvability, we discuss the structural frontiers and research challenges, and we conclude with an outlook.作者: FECT 時(shí)間: 2025-3-28 15:00
https://doi.org/10.1057/9781137331175relationship with the existing state-of-the-art unsupervised learning models. However, as a new and developing technology, there are still many interesting open issues remained unsolved and waiting for research from theoretical and algorithmic perspectives.作者: 客觀 時(shí)間: 2025-3-28 21:49
Views from the Neighbourhood: Israelhere patterns are refined. The approach is illustrated with application to the medical, law enforcement and security domains. The medical application is concerned with discovering breast cancer diagnostic rules (i) interactively with a radiologist, (ii) analytically with data mining algorithms, and 作者: 彩色的蠟筆 時(shí)間: 2025-3-29 02:16 作者: 愛哭 時(shí)間: 2025-3-29 04:16 作者: LIEN 時(shí)間: 2025-3-29 07:55
Data Mining with Multilayer Perceptrons and Support Vector Machines,s are the most popular neural network type, consisting on a feedforward network of processing neurons that are grouped into layers and connected by weighted links. On the other hand, SVM transforms the input variables into a high dimensional feature space and then finds the best hyperplane that mode作者: 痛得哭了 時(shí)間: 2025-3-29 11:33
