標(biāo)題: Titlebook: Data Mining Methods for Knowledge Discovery; Krzysztof J. Cios,Witold Pedrycz,Roman W. Swiniars Book 1998 Springer Science+Business Media [打印本頁(yè)] 作者: Adentitious 時(shí)間: 2025-3-21 19:41
書目名稱Data Mining Methods for Knowledge Discovery影響因子(影響力)
書目名稱Data Mining Methods for Knowledge Discovery影響因子(影響力)學(xué)科排名
書目名稱Data Mining Methods for Knowledge Discovery網(wǎng)絡(luò)公開度
書目名稱Data Mining Methods for Knowledge Discovery網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data Mining Methods for Knowledge Discovery被引頻次
書目名稱Data Mining Methods for Knowledge Discovery被引頻次學(xué)科排名
書目名稱Data Mining Methods for Knowledge Discovery年度引用
書目名稱Data Mining Methods for Knowledge Discovery年度引用學(xué)科排名
書目名稱Data Mining Methods for Knowledge Discovery讀者反饋
書目名稱Data Mining Methods for Knowledge Discovery讀者反饋學(xué)科排名
作者: Ventilator 時(shí)間: 2025-3-21 23:23
0893-3405 nowledge discovery. This book first elaborates on thefundamentals of each of the data mining methods: rough sets, Bayesiananalysis, fuzzy sets, genetic algorithms, machine learning, neuralnetworks, and preprocessing techniques. The book then goes on tothoroughly discuss these methods in the setting 作者: Antagonism 時(shí)間: 2025-3-22 01:56 作者: etidronate 時(shí)間: 2025-3-22 05:04
Book 1998liography. ..Data Mining Methods for Knowledge Discovery. is intended forsenior undergraduate and graduate students, as well as a broadaudience of professionals in computer and information sciences,medical informatics, and business information systems.作者: Presbycusis 時(shí)間: 2025-3-22 11:45
0893-3405 ensive bibliography. ..Data Mining Methods for Knowledge Discovery. is intended forsenior undergraduate and graduate students, as well as a broadaudience of professionals in computer and information sciences,medical informatics, and business information systems.978-1-4613-7557-9978-1-4615-5589-6Series ISSN 0893-3405 作者: RAFF 時(shí)間: 2025-3-22 15:48
https://doi.org/10.1007/978-3-031-57373-6he most representative examples of the principle of evolutionary computing. Owing to the generality of evolutionary computing and a lack of specific assumptions about a problem to be tackled, genetic algorithms are capable of dealing with a broad class of tasks in spite of their formulation and the nature of the optimization to be completed.作者: RAFF 時(shí)間: 2025-3-22 19:19
Evolutionary Computing,he most representative examples of the principle of evolutionary computing. Owing to the generality of evolutionary computing and a lack of specific assumptions about a problem to be tackled, genetic algorithms are capable of dealing with a broad class of tasks in spite of their formulation and the nature of the optimization to be completed.作者: 遠(yuǎn)地點(diǎn) 時(shí)間: 2025-3-23 00:56
Fuzzy Sets,ets, and related concepts of shadowed sets and rough sets. We highlight differences between computing with fuzzy sets and probabilities. Furthermore, we exhaustively revisit a concept of information granularity as emerging in fuzzy sets that constitutes a key notion of efficient machinery of data mining.作者: guzzle 時(shí)間: 2025-3-23 02:57
Bayesian Methods,ues of probability densities used in Bayesian inference. Finally the probabilistic neural network PNN, as a hardware implementation of kernel-based probability density and Bayesian classification, is discussed.作者: 不規(guī)則 時(shí)間: 2025-3-23 08:54 作者: 開花期女 時(shí)間: 2025-3-23 11:20
Machine Learning,ithms for both. Next, we describe an algorithm representing a family of hybrid algorithms combining the two approaches. In Appendix A6 we give a comprehensive example using coronary artery disease data that involves many of the data mining methods described in this book.作者: overweight 時(shí)間: 2025-3-23 13:56 作者: anatomical 時(shí)間: 2025-3-23 18:45
https://doi.org/10.1007/978-3-030-45807-2ues of probability densities used in Bayesian inference. Finally the probabilistic neural network PNN, as a hardware implementation of kernel-based probability density and Bayesian classification, is discussed.作者: 清醒 時(shí)間: 2025-3-24 00:45
Frameworks for CIS Research and Developmentinear discriminant and linear transformation. We also provide the sequence of PCA and Fisher’s transformation for feature extraction and reduction. Finally, results of numerical experiments related to texture image classification, including feature extraction and selection, are described.作者: 變形詞 時(shí)間: 2025-3-24 02:28 作者: 焦慮 時(shí)間: 2025-3-24 07:08 作者: Mirage 時(shí)間: 2025-3-24 13:38
Krzysztof J. Cios,Witold Pedrycz,Roman W. Swiniars作者: GROWL 時(shí)間: 2025-3-24 17:03 作者: Incommensurate 時(shí)間: 2025-3-24 20:51
Introduction: Collaborative Governance,aries. We outline the underlying concepts and theory, both of them placed in the setting of data mining. First, we start with some basic definitions and characterizations of fuzzy sets. Afterwards we move on to more technical content dealing with membership function estimation, operations on fuzzy s作者: STRIA 時(shí)間: 2025-3-25 00:49
https://doi.org/10.1007/978-3-030-45807-2simple two-class pattern classification. Then we will generalize it for multifeature and multiclass pattern classification. We will also discuss classifier design based on discriminant functions for normally distributed probabilities of patterns. Furthermore, we will discuss major estimation techniq作者: intolerance 時(shí)間: 2025-3-25 05:37 作者: 無所不知 時(shí)間: 2025-3-25 08:05 作者: 一大群 時(shí)間: 2025-3-25 13:24
Collaborative Governance Primerhms covered are chosen based on their potential for analysis of large amounts of numerical data or images. Images are becoming increasingly more popular as a mode of data collection and neural networks have proven to be very effective in dealing with image data.作者: encyclopedia 時(shí)間: 2025-3-25 16:15 作者: 游行 時(shí)間: 2025-3-25 22:05
Frameworks for CIS Research and Developmentcessing sequences in KD. Next, we briefly describe basic preprocessing operations. Then we study feature selection methods. As an illustration of major preprocessing operations we describe the Principal Component Analysis (PCA) for pattern projection, feature extraction and reduction, and Fisher’s l作者: 鍵琴 時(shí)間: 2025-3-26 03:34
https://doi.org/10.1007/978-3-030-45807-2This chapter attempts a concise introduction to data mining and knowledge discovery. First, we introduce the necessary nomenclature and definitions, discuss the background of the area, and elaborate on the technologies constituting the core part of knowledge discovery. Then we discuss several representative examples of knowledge discovery systems.作者: FACT 時(shí)間: 2025-3-26 05:25
Data Mining and Knowledge Discovery,This chapter attempts a concise introduction to data mining and knowledge discovery. First, we introduce the necessary nomenclature and definitions, discuss the background of the area, and elaborate on the technologies constituting the core part of knowledge discovery. Then we discuss several representative examples of knowledge discovery systems.作者: 無目標(biāo) 時(shí)間: 2025-3-26 12:05 作者: MEAN 時(shí)間: 2025-3-26 13:31
Neural Networks,hms covered are chosen based on their potential for analysis of large amounts of numerical data or images. Images are becoming increasingly more popular as a mode of data collection and neural networks have proven to be very effective in dealing with image data.作者: needle 時(shí)間: 2025-3-26 19:12
Data Mining Methods for Knowledge Discovery978-1-4615-5589-6Series ISSN 0893-3405 作者: 隨意 時(shí)間: 2025-3-27 00:02 作者: 分期付款 時(shí)間: 2025-3-27 02:34
Collaborative Governance Primerhms covered are chosen based on their potential for analysis of large amounts of numerical data or images. Images are becoming increasingly more popular as a mode of data collection and neural networks have proven to be very effective in dealing with image data.作者: 可能性 時(shí)間: 2025-3-27 06:06
https://doi.org/10.1007/978-1-4615-5589-6algorithms; data mining; evolution; evolutionary computation; fuzzy; fuzzy sets; genetic algorithms; inform作者: Oafishness 時(shí)間: 2025-3-27 11:59
978-1-4613-7557-9Springer Science+Business Media New York 1998作者: Exuberance 時(shí)間: 2025-3-27 16:32
The Springer International Series in Engineering and Computer Sciencehttp://image.papertrans.cn/d/image/262906.jpg作者: 頌揚(yáng)本人 時(shí)間: 2025-3-27 21:28
Rough Sets,ajor ideas and definition of rough sets for processing uncertain data, discovering dependencies, approximation of data, classification, measuring attribute significance, reducing data, and designing decision rules.作者: entreat 時(shí)間: 2025-3-27 23:18
Fuzzy Sets,aries. We outline the underlying concepts and theory, both of them placed in the setting of data mining. First, we start with some basic definitions and characterizations of fuzzy sets. Afterwards we move on to more technical content dealing with membership function estimation, operations on fuzzy s作者: Apogee 時(shí)間: 2025-3-28 03:10
Bayesian Methods,simple two-class pattern classification. Then we will generalize it for multifeature and multiclass pattern classification. We will also discuss classifier design based on discriminant functions for normally distributed probabilities of patterns. Furthermore, we will discuss major estimation techniq作者: Apoptosis 時(shí)間: 2025-3-28 09:49
Evolutionary Computing, and Cheng 1997; Michalewicz 1992; Schwefel 195). In contrast to standard methods of nonlinear optimization (Horst and Pardalos 1995) that rely on a single — point search (that is a migration of a single element across the search space), evolutionary computing exploits an entire population of potent作者: 終點(diǎn) 時(shí)間: 2025-3-28 14:11
Machine Learning,m data. We review two major approaches to inductive machine learning, rule algorithms and decision tree algorithms, by describing representative algorithms for both. Next, we describe an algorithm representing a family of hybrid algorithms combining the two approaches. In Appendix A6 we give a compr作者: Feedback 時(shí)間: 2025-3-28 15:34 作者: interrupt 時(shí)間: 2025-3-28 22:10 作者: 怪物 時(shí)間: 2025-3-29 01:45 作者: indignant 時(shí)間: 2025-3-29 05:38
IAM in La Sarraz 1928 und der UIA 1948 genannt. Zu La Sarraz vgl. Martin Steinmann (1979), S. 11–33. Mit der UIA-Gründung besch?ftigt sich der Abschnitt ?Selbstbilder“ in dieser Arbeit. 5 Tournikiotis (2002) 6 Frampton (2002) 7 Brasilia ist die vierte Fallstudie dieser Arbeit gewidmet. 8 Osborn/Whit
