標(biāo)題: Titlebook: ; [打印本頁] 作者: Inspection 時(shí)間: 2025-3-21 18:14
書目名稱Guide to Intelligent Data Analysis影響因子(影響力)
書目名稱Guide to Intelligent Data Analysis影響因子(影響力)學(xué)科排名
書目名稱Guide to Intelligent Data Analysis網(wǎng)絡(luò)公開度
書目名稱Guide to Intelligent Data Analysis網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Guide to Intelligent Data Analysis被引頻次
書目名稱Guide to Intelligent Data Analysis被引頻次學(xué)科排名
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書目名稱Guide to Intelligent Data Analysis讀者反饋
書目名稱Guide to Intelligent Data Analysis讀者反饋學(xué)科排名
作者: 難取悅 時(shí)間: 2025-3-21 21:17
https://doi.org/10.1057/9780230582194pitfalls one encounters when analyzing real-world data. We start our journey through the data analysis process by looking over the shoulders of two (pseudo) data analysts, Stan and Laura, working on some hypothetical data analysis problems in a sales environment. Being differently skilled, they show作者: 盡忠 時(shí)間: 2025-3-22 03:32 作者: Myocyte 時(shí)間: 2025-3-22 05:07 作者: 強(qiáng)行引入 時(shí)間: 2025-3-22 09:32
The French: A Cross-cultural Comparison,ject understanding phase to revise objectives (or to stop the project). In the former case, we have to prepare the dataset for subsequent modeling. However, as some of the data preparation steps are motivated by modeling itself, we first discuss the principles of modeling. Many modeling methods will作者: 食物 時(shí)間: 2025-3-22 16:48
Mark Burton,Carolyn Kagan,Pat Clementsations. We intend to apply various modeling techniques to extract models from the data. Although we have not yet discussed any modeling technique in greater detail (see Chaps.?7ff), we have already glimpsed at some fundamental techniques and potential pitfalls in the previous chapter. Before we star作者: 食物 時(shí)間: 2025-3-22 18:33
Florian Mayer,Dennis Schoeneborn the identification of areas that exceptionally deviate from the remainder. They provide answers to questions such as: Does it naturally subdivide into groups? How do attributes depend on each other? Are there certain conditions leading to exceptions from the average behaviour? The chapter provides 作者: 觀點(diǎn) 時(shí)間: 2025-3-23 00:18 作者: Popcorn 時(shí)間: 2025-3-23 04:46
https://doi.org/10.1007/978-3-031-52399-1e discussed methods for basically the same purpose, the methods in this chapter yield models that do not help much to explain the data or even dispense with models altogether. Nevertheless, they can be useful, namely if the main goal is good prediction accuracy rather than an intuitive and interpret作者: Rheumatologist 時(shí)間: 2025-3-23 08:23
The Measurement of Stratificationsures such as classification accuracy has been checked routinely whenever changes to the model were made to judge the advantageousness of the modifications. The models were also interpreted to gain new insights for feature construction (or even data acquisition). Once we are satisfied with the techn作者: 完全 時(shí)間: 2025-3-23 11:30
Introduction,refully distinguish between “data” and “knowledge” in order to obtain clear notions that help us to work out why it is usually not enough to simply collect data and why we have to strive to turn them into knowledge. As an illustration, we consider a well-known example from the history of science. In作者: Chagrin 時(shí)間: 2025-3-23 14:45 作者: BIAS 時(shí)間: 2025-3-23 20:34 作者: narcotic 時(shí)間: 2025-3-24 01:59
Data Understanding,ysis process, but data understanding should not be driven exclusively by the goals and methods to be applied in later steps. Although these requirements should be kept in mind during data understanding, one should approach the data from a neutral point of view. Never trust any data as long as you ha作者: 打包 時(shí)間: 2025-3-24 02:36 作者: WAG 時(shí)間: 2025-3-24 09:05
Data Preparation,ations. We intend to apply various modeling techniques to extract models from the data. Although we have not yet discussed any modeling technique in greater detail (see Chaps.?7ff), we have already glimpsed at some fundamental techniques and potential pitfalls in the previous chapter. Before we star作者: TEM 時(shí)間: 2025-3-24 14:02 作者: arterioles 時(shí)間: 2025-3-24 15:51
Finding Explanations, in order to group similar objects. In this chapter we will discuss methods that address a very different setup: instead of finding structure in a data set, we are now focusing on methods that find explanations for an unknown dependency within the data. Such a search for a dependency usually focuses作者: Conjuction 時(shí)間: 2025-3-24 21:35
Finding Predictors,e discussed methods for basically the same purpose, the methods in this chapter yield models that do not help much to explain the data or even dispense with models altogether. Nevertheless, they can be useful, namely if the main goal is good prediction accuracy rather than an intuitive and interpret作者: 加花粗鄙人 時(shí)間: 2025-3-25 01:15 作者: 訓(xùn)誡 時(shí)間: 2025-3-25 06:03 作者: avarice 時(shí)間: 2025-3-25 10:16
Data Preparation,reater detail (see Chaps.?7ff), we have already glimpsed at some fundamental techniques and potential pitfalls in the previous chapter. Before we start modeling, we have to prepare our data set appropriately, that is, we are going to modify our dataset so that the modeling techniques are best supported but least biased.作者: motor-unit 時(shí)間: 2025-3-25 13:35 作者: geometrician 時(shí)間: 2025-3-25 17:12
Florian Mayer,Dennis Schoenebornan overview of clustering methods (hierarchical clustering, k-Means, density-based clustering), association analysis, self-organizing maps and deviation analysis. The definition and choice of distance or similarity measures, which is required by almost every technique to compare different cases in the database, is also tackled.作者: carbohydrate 時(shí)間: 2025-3-25 22:17 作者: 合唱隊(duì) 時(shí)間: 2025-3-26 02:28 作者: 芭蕾舞女演員 時(shí)間: 2025-3-26 08:22 作者: Innocence 時(shí)間: 2025-3-26 09:54
https://doi.org/10.1007/978-94-011-7819-8erstanding phase, we know much better whether the assumptions we made during the project understanding phase concerning representativeness, informativeness, data quality, and the presence or absence of external factors are justified.作者: 游行 時(shí)間: 2025-3-26 14:22 作者: ascend 時(shí)間: 2025-3-26 17:13 作者: 小木槌 時(shí)間: 2025-3-26 23:17 作者: temperate 時(shí)間: 2025-3-27 03:22
Evaluation and Deployment,tackle these two steps only briefly in the following two sections. A deployment in the form of, say, a software system for decision support involves several planning and coordination tasks, which are out of the scope of this book.作者: 致敬 時(shí)間: 2025-3-27 05:16
My Life in the L-café from Different Angles Examples for such problems would be understanding why a customer belongs to the category of people who cancel their account (e.g., classifying her into a yes/no category) or better understanding the risk factors of customers in general.作者: 演講 時(shí)間: 2025-3-27 10:23
https://doi.org/10.1007/978-3-031-52399-1 to the application domain, the models they yield are basically “black boxes” and almost impossible to interpret in terms of the application domain. Hence they should be considered only if a comprehensible model that can easily be checked for plausibility is not required, and high accuracy is the main concern.作者: 物質(zhì) 時(shí)間: 2025-3-27 15:35
Finding Explanations, Examples for such problems would be understanding why a customer belongs to the category of people who cancel their account (e.g., classifying her into a yes/no category) or better understanding the risk factors of customers in general.作者: Stress 時(shí)間: 2025-3-27 20:18 作者: pineal-gland 時(shí)間: 2025-3-28 01:49
The French: A Cross-cultural Comparison,wever, as some of the data preparation steps are motivated by modeling itself, we first discuss the principles of modeling. Many modeling methods will be introduced in the following chapters, but this chapter is devoted to problems and aspects that are inherent in and common to all the methods for analyzing the data.作者: 狗舍 時(shí)間: 2025-3-28 04:02
Mark Burton,Carolyn Kagan,Pat Clementsreater detail (see Chaps.?7ff), we have already glimpsed at some fundamental techniques and potential pitfalls in the previous chapter. Before we start modeling, we have to prepare our data set appropriately, that is, we are going to modify our dataset so that the modeling techniques are best supported but least biased.作者: Tinea-Capitis 時(shí)間: 2025-3-28 06:46
Guide to Intelligent Data Analysis978-1-84882-260-3Series ISSN 1868-0941 Series E-ISSN 1868-095X 作者: Repatriate 時(shí)間: 2025-3-28 13:42 作者: Estrogen 時(shí)間: 2025-3-28 16:18 作者: Gullible 時(shí)間: 2025-3-28 21:59
Project Understanding,apidly expanding, the failure rate is still high, so this phase should be carried out seriously to rate the chances of success realistically. The project understanding phase should be carried out with care to keep the project on the right track.作者: Epithelium 時(shí)間: 2025-3-29 01:38 作者: 大雨 時(shí)間: 2025-3-29 05:39 作者: 馬具 時(shí)間: 2025-3-29 10:57
Exploring Plant Co-Expression and Gene-Gene Interactions with CORNET 3.0,the network, new target and regulator genes can be discovered, allowing for follow-up experiments and more in-depth study. We also indicate how to avoid common pitfalls when evaluating the networks and how to avoid over interpretation of the results..All CORNET versions are available at ..作者: 暫時(shí)休息 時(shí)間: 2025-3-29 15:25 作者: 吝嗇性 時(shí)間: 2025-3-29 19:16
Multi-party Interaction in Public Spaces: Cross-Cultural Variations in Parental and Nonparental Resesults include a number of interesting findings based on people’s relationship with a child and their parental status. In addition, a number of cross-cultural differences were identified in respondents’ attitudes toward robot’s multi-party adaptation in various public settings.作者: 滑稽 時(shí)間: 2025-3-29 22:02
https://doi.org/10.1007/978-1-59745-444-5Psychodynamic; Psychotherapy; depression; neurobiology; psychiatry; psychodynamics; psychology
