標(biāo)題: Titlebook: Stream Data Mining: Algorithms and Their Probabilistic Properties; Leszek Rutkowski,Maciej Jaworski,Piotr Duda Book 2020 Springer Nature S [打印本頁] 作者: Reagan 時(shí)間: 2025-3-21 17:22
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作者: Urea508 時(shí)間: 2025-3-21 22:18
s are reviewed here, as is the use of biological annotation for both viewing the relevance of empirical associations, and to structure analysis in order to focus on those markers with the highest expectation for association with the outcomes under study.作者: 異常 時(shí)間: 2025-3-22 03:12
Leszek Rutkowski,Maciej Jaworski,Piotr Dudas are reviewed here, as is the use of biological annotation for both viewing the relevance of empirical associations, and to structure analysis in order to focus on those markers with the highest expectation for association with the outcomes under study.作者: 正面 時(shí)間: 2025-3-22 05:31
Leszek Rutkowski,Maciej Jaworski,Piotr Dudas are reviewed here, as is the use of biological annotation for both viewing the relevance of empirical associations, and to structure analysis in order to focus on those markers with the highest expectation for association with the outcomes under study.作者: 在前面 時(shí)間: 2025-3-22 11:11 作者: Factorable 時(shí)間: 2025-3-22 14:33
Leszek Rutkowski,Maciej Jaworski,Piotr Dudas are reviewed here, as is the use of biological annotation for both viewing the relevance of empirical associations, and to structure analysis in order to focus on those markers with the highest expectation for association with the outcomes under study.作者: 贊成你 時(shí)間: 2025-3-22 18:52
Leszek Rutkowski,Maciej Jaworski,Piotr Dudas are reviewed here, as is the use of biological annotation for both viewing the relevance of empirical associations, and to structure analysis in order to focus on those markers with the highest expectation for association with the outcomes under study.作者: 轎車 時(shí)間: 2025-3-22 22:34
2197-6503 oosing their sizes is described and solved. Given its scope, the book is intended for a professional audience of researchers and practitioners who dealwith stream data, e.g. in telecommunication, banking, and sensor networks..978-3-030-13962-9Series ISSN 2197-6503 Series E-ISSN 2197-6511 作者: bronchodilator 時(shí)間: 2025-3-23 03:39
Introduction and Overview of the Main Results of the Book,y of the previously presented in the literature heuristic methods, this book focuses on algorithms which are mathematically justified. However, it should be noted that the heuristic solutions cannot be completely abandoned since they often lead to satisfactory practical results. Therefore, the mathe作者: 斷斷續(xù)續(xù) 時(shí)間: 2025-3-23 08:07 作者: 旁觀者 時(shí)間: 2025-3-23 11:46 作者: DEFER 時(shí)間: 2025-3-23 16:07 作者: inspiration 時(shí)間: 2025-3-23 18:55
select, implement, and analyze a group sequential stopping rule. Throughout, we illustrate trial design and monitoring in the context of a group sequential survival trial of an experimental monoclonal antibody in patients with relapsed chronic lymphocytic leukemia (CLL).作者: irreparable 時(shí)間: 2025-3-24 00:27 作者: Vsd168 時(shí)間: 2025-3-24 03:09
Book 2020y, an extremely challenging problem that involves designing ensembles and automatically choosing their sizes is described and solved. Given its scope, the book is intended for a professional audience of researchers and practitioners who dealwith stream data, e.g. in telecommunication, banking, and sensor networks..作者: 在駕駛 時(shí)間: 2025-3-24 10:06
Hybrid Splitting Criteria a new kind of splitting criteria can be proposed, which combine together two different single criteria in a heuristic manner. We refer to them as hybrid splitting criteria. In this chapter, we discuss premises which demonstrate that such an approach may lead to satisfactory results.作者: ABIDE 時(shí)間: 2025-3-24 11:18
Classificationoduced modification allows increasing the diversity of the ensemble components. The problem of selection component is an essential issue for every ensemble algorithm [.,.,.,.,.,.,.,.], however, only few of them are not heuristic procedures [., .].作者: HALO 時(shí)間: 2025-3-24 18:34 作者: Gingivitis 時(shí)間: 2025-3-24 19:07 作者: multiply 時(shí)間: 2025-3-25 03:00
Misclassification Error Impurity Measureum of random variables. In this chapter, a split measure based on the misclassification error impurity measure is proposed [., .], which has the mentioned above property. In the case of misclassification error, the bounds obtained using the Hoeffding’s inequality and the McDiarmid’s inequality are equivalent.作者: 四牛在彎曲 時(shí)間: 2025-3-25 05:24
The General Procedure of Ensembles Construction in Data Stream Scenariossulting with a few various persons. The vivid example is the diagnosis of an illness. When someone gets bad news, he often goes to other doctors for a second, third, fourth opinion and so on, until we are sure about the diagnosis.作者: Itinerant 時(shí)間: 2025-3-25 07:55 作者: 玩忽職守 時(shí)間: 2025-3-25 11:53
Basic Concepts of Probabilistic Neural Networks we should say that these methods have been almost completely replaced by the non-parametric approach (see e.g. [.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.]). In the non-parametric approach it is assumed that a functional form of probability densities is unknown.作者: Ethics 時(shí)間: 2025-3-25 17:21
2197-6503 heuristics, it highlights methods and algorithms that are ma.This book presents a unique approach to stream data mining. Unlike the vast majority of previous approaches, which are largely based on heuristics, it highlights methods and algorithms that are mathematically justified. First, it describes作者: 箴言 時(shí)間: 2025-3-25 21:49 作者: 恩惠 時(shí)間: 2025-3-26 03:19 作者: cochlea 時(shí)間: 2025-3-26 06:49 作者: 分離 時(shí)間: 2025-3-26 11:42 作者: explicit 時(shí)間: 2025-3-26 14:59 作者: Tartar 時(shí)間: 2025-3-26 20:35
Misclassification Error Impurity Measureequality is the application of another statistical tool, e.g. the McDiarmid’s inequality. Another way is to find a split measure which can be expressed as an arithmetic average of some random variables since the Hoeffding’s inequality is applicable in this case. In the literature, many different imp作者: Throttle 時(shí)間: 2025-3-26 21:10
Hybrid Splitting Criteria measure (or, more precisely, the corresponding split measure function). Therefore we will refer to such criteria as ‘single’ splitting criteria. The experiments conducted in Chap.?. demonstrate that various single splitting criteria have their own advantages and drawbacks. Based on this observation作者: apiary 時(shí)間: 2025-3-27 01:56
Basic Concepts of Probabilistic Neural Networksifties and sixties, problems of statistical pattern classification in the stationary case were accomplished by means of parametric methods, using the available apparatus of statistical mathematics (e.g. [.,.,.,.,.]). The knowledge of the probability density to an accuracy of unknown parameters was a作者: Femish 時(shí)間: 2025-3-27 06:38
General Non-parametric Learning Procedure for Tracking Concept Drift learning in non-stationary environments where occasionally published in the sixties and seventies. The proper tool for solving such a type of problems seemed to be the dynamic stochastic approximation technique [., .] as an extension of the Robbins-Monro [.] procedure for the non-stationary case. T作者: 拍下盜公款 時(shí)間: 2025-3-27 10:34
Nonparametric Regression Models for Data Streams Based on the Generalized Regression Neural Networksthem deal with a non-stationary regression. Most of them rely on the Gaussian or Markov models, extend Support Vector Machine or Extreme Learning Machine to regression problems, implement regression trees or polynomial regression for working in a non-stationary environment. We will briefly describe 作者: 手榴彈 時(shí)間: 2025-3-27 16:59
Probabilistic Neural Networks for the Streaming Data Classificationhough there exist a lot of methods for classification of static datasets, they can hardly be adapted to deal with data streams. This is due to the features of the data stream such as potentially infinite volume, fast rate of data arrival and the occurrence of concept drift.作者: 窗簾等 時(shí)間: 2025-3-27 18:12
The General Procedure of Ensembles Construction in Data Stream Scenarios. However, in many cases, the fastest algorithms are less accurate than methods requiring high computational power and more time for data analysis. Therefore, to enhance the performance of the algorithms, which in data stream scenario must be characterized by low memory requirement and short time of作者: 驚呼 時(shí)間: 2025-3-28 01:32 作者: 使痛苦 時(shí)間: 2025-3-28 03:06
Regression is a lack of new approaches to creating ensembles of regression estimators [., .]. Most of the latest developments focus on the application of the regression estimators to solve very important real-world problems. In [.] the authors propose to create an ensemble composed of decision trees, gradient作者: 邊緣帶來墨水 時(shí)間: 2025-3-28 09:09 作者: 愛社交 時(shí)間: 2025-3-28 10:38
Leszek Rutkowski,Maciej Jaworski,Piotr Dudar clinical trials. This chapter offers a unified basis for the analysis of marker and response data, emphasizing the central importance of the correlation, or linkage disequilibrium, between SNP markers and the genes that affect response. It is convenient to phrase the development of association map作者: modest 時(shí)間: 2025-3-28 14:47 作者: hysterectomy 時(shí)間: 2025-3-28 19:33 作者: 討好美人 時(shí)間: 2025-3-29 01:03
Leszek Rutkowski,Maciej Jaworski,Piotr Dudar clinical trials. This chapter offers a unified basis for the analysis of marker and response data, emphasizing the central importance of the correlation, or linkage disequilibrium, between SNP markers and the genes that affect response. It is convenient to phrase the development of association map作者: Enrage 時(shí)間: 2025-3-29 03:53 作者: GUEER 時(shí)間: 2025-3-29 09:38
Leszek Rutkowski,Maciej Jaworski,Piotr Dudar clinical trials. This chapter offers a unified basis for the analysis of marker and response data, emphasizing the central importance of the correlation, or linkage disequilibrium, between SNP markers and the genes that affect response. It is convenient to phrase the development of association map作者: 破布 時(shí)間: 2025-3-29 11:27 作者: 前奏曲 時(shí)間: 2025-3-29 19:28
istical, and logistical constraints, it is important to carefully evaluate candidate group sequential designs to ensure desirable operating characteristics. At the implementation stage of a clinical trial design it is also essential to account for deviations from original design specifications in or作者: 衍生 時(shí)間: 2025-3-29 19:44
Leszek Rutkowski,Maciej Jaworski,Piotr Dudar clinical trials. This chapter offers a unified basis for the analysis of marker and response data, emphasizing the central importance of the correlation, or linkage disequilibrium, between SNP markers and the genes that affect response. It is convenient to phrase the development of association map作者: Microgram 時(shí)間: 2025-3-30 00:43
Splitting Criteria with the Bias TermThe Mean Squared Error (MSE) of any estimator . of some quantity . is a sum of two terms 作者: 內(nèi)疚 時(shí)間: 2025-3-30 06:01
Leszek Rutkowski,Maciej Jaworski,Piotr DudaPresents a unique and innovative approach to stream data mining.Unlike the vast majority of previous approaches, which are largely based on heuristics, it highlights methods and algorithms that are ma作者: 扔掉掐死你 時(shí)間: 2025-3-30 11:52
Springer Nature Switzerland AG 2020作者: Infect 時(shí)間: 2025-3-30 12:35 作者: enchant 時(shí)間: 2025-3-30 20:28
Decision Trees in Data Stream Mining produced by decision trees are easily interpretable. A decision tree, in fact, divides attribute values space . into disjoint subspaces. The most common decision tree induction algorithms for static data sets are the ID3 algorithm [.], the C4.5 algorithm [., .], and the CART algorithm [.].作者: 四指套 時(shí)間: 2025-3-30 21:49 作者: bypass 時(shí)間: 2025-3-31 04:28
Probabilistic Neural Networks for the Streaming Data Classificationhough there exist a lot of methods for classification of static datasets, they can hardly be adapted to deal with data streams. This is due to the features of the data stream such as potentially infinite volume, fast rate of data arrival and the occurrence of concept drift.
