標(biāo)題: Titlebook: Computing Statistics under Interval and Fuzzy Uncertainty; Applications to Comp Hung T. Nguyen,Vladik Kreinovich,Gang Xiang Book 20121st ed [打印本頁(yè)] 作者: polysomnography 時(shí)間: 2025-3-21 16:46
書(shū)目名稱Computing Statistics under Interval and Fuzzy Uncertainty影響因子(影響力)
書(shū)目名稱Computing Statistics under Interval and Fuzzy Uncertainty影響因子(影響力)學(xué)科排名
書(shū)目名稱Computing Statistics under Interval and Fuzzy Uncertainty網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Computing Statistics under Interval and Fuzzy Uncertainty網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Computing Statistics under Interval and Fuzzy Uncertainty被引頻次
書(shū)目名稱Computing Statistics under Interval and Fuzzy Uncertainty被引頻次學(xué)科排名
書(shū)目名稱Computing Statistics under Interval and Fuzzy Uncertainty年度引用
書(shū)目名稱Computing Statistics under Interval and Fuzzy Uncertainty年度引用學(xué)科排名
書(shū)目名稱Computing Statistics under Interval and Fuzzy Uncertainty讀者反饋
書(shū)目名稱Computing Statistics under Interval and Fuzzy Uncertainty讀者反饋學(xué)科排名
作者: 一瞥 時(shí)間: 2025-3-21 22:57 作者: 原始 時(shí)間: 2025-3-22 03:51
Types of Interval Data Sets: Towards Feasible Algorithmschapter shows, computing variance . under interval uncertainty is, in general, an NP-hard (computationally difficult) problem. As we will see in the following chapters, a similar problem is NP-hard for many other statistical characteristics . as well.作者: 卵石 時(shí)間: 2025-3-22 05:42 作者: 寬宏大量 時(shí)間: 2025-3-22 10:54 作者: 不在灌木叢中 時(shí)間: 2025-3-22 15:56 作者: 不在灌木叢中 時(shí)間: 2025-3-22 20:51
Martin S. Olivier,Sujeet Shenoi corresponding to a . from the population. Based on these sample values, we need to estimate . characteristics . – i.e., characteristics that describe the population as a whole, such as the mean and the variance of different quantities, the correlation between different quantities, etc.作者: 外露 時(shí)間: 2025-3-23 00:06
Formulation of the Problem corresponding to a . from the population. Based on these sample values, we need to estimate . characteristics . – i.e., characteristics that describe the population as a whole, such as the mean and the variance of different quantities, the correlation between different quantities, etc.作者: 我不死扛 時(shí)間: 2025-3-23 01:39 作者: Liberate 時(shí)間: 2025-3-23 07:28 作者: 轉(zhuǎn)向 時(shí)間: 2025-3-23 11:08 作者: 谷類 時(shí)間: 2025-3-23 14:27
1860-949X ng.In many practical situations, we are interested in statistics characterizing a population of objects: e.g. in the mean height of people from a certain area..?.Most algorithms for estimating such statistics assume that the sample values are exact. In practice, sample values come from measurements,作者: 運(yùn)動(dòng)性 時(shí)間: 2025-3-23 20:41
Disk Drive I/O Commands and Write Blockingir estimates are described by (imprecise, “fuzzy”) words from natural language. For example, an expert can say that the value . of the .-th quantity is approximately equal to 1.0, with an accuracy most probably of about 0.1.作者: 玩笑 時(shí)間: 2025-3-24 00:24
https://doi.org/10.1007/978-3-642-41148-9puting, for every possible action ., the corresponding expected utility. To be more precise, we usually know, for each action . and for each actual value of the (unknown) quantity ., the corresponding value of the utility .(.). We must use the probability distribution for . to compute the expected value e[.(.)] of this utility.作者: PUT 時(shí)間: 2025-3-24 03:34
Digital Forensics as a Surreal Narrativechapter shows, computing variance . under interval uncertainty is, in general, an NP-hard (computationally difficult) problem. As we will see in the following chapters, a similar problem is NP-hard for many other statistical characteristics . as well.作者: DUST 時(shí)間: 2025-3-24 09:26 作者: syncope 時(shí)間: 2025-3-24 11:04
Computing Statistics under Interval and Fuzzy Uncertainty978-3-642-24905-1Series ISSN 1860-949X Series E-ISSN 1860-9503 作者: 反省 時(shí)間: 2025-3-24 15:36 作者: 媒介 時(shí)間: 2025-3-24 20:10 作者: 調(diào)色板 時(shí)間: 2025-3-25 00:25
A Compiled Memory Analysis Toolnt . can be always computed in feasible (polynomial) time). Since we cannot always efficiently compute the upper endpoint . , we therefore need to consider cases when such an efficient computation may be possible.作者: FATAL 時(shí)間: 2025-3-25 04:36
https://doi.org/10.1007/978-3-642-24905-1Fuzziness; Fuzzy Uncertainty; Interval Uncertainty; Soft Computing作者: 最低點(diǎn) 時(shí)間: 2025-3-25 09:28 作者: 絕種 時(shí)間: 2025-3-25 12:51
Martin S. Olivier,Sujeet Shenointities characterizing objects from this population. For example, we are interested in the human population in a certain region, and we are interested in their heights, weights, etc..Different objects from a population have, in general, different values of the desired characteristics. Measuring, sto作者: 溫順 時(shí)間: 2025-3-25 17:24 作者: Insensate 時(shí)間: 2025-3-25 23:52
Advances in Digital Forensics IIIrst reformulate fuzzy techniques in an interval-related form..In some situations, an expert knows exactly which values of . are possible and which are not. In this situation, the expert’s knowledge can be naturally represented by describing the set of all possible values.作者: 公社 時(shí)間: 2025-3-26 02:34
Advances in Digital Forensics IIIsome applications, it is important to guarantee that the (unknown) actual value . of a certain quantity does not exceed a certain threshold .0. The only way to guarantee this is to have an interval . = [., . ] which is guaranteed to contain . (i.e., for which . ? . ) and for which . ≤ .0.作者: TEN 時(shí)間: 2025-3-26 05:05
Stephen Esposito,Gilbert Petersonis means, crudely speaking, that it is not possible to design a feasible algorithm that would compute all statistics under interval uncertainty. It is therefore necessary to restrict ourselves to statistical characteristics which are practically useful..Which statistical characteristics should we es作者: Intercept 時(shí)間: 2025-3-26 10:19 作者: Abnormal 時(shí)間: 2025-3-26 15:22
Digital Forensics as a Surreal Narrative.In Chapter 4, we have explained that the problem of computing these values under fuzzy uncertainty can be reduced to the problem of computing the values of this characteristic under interval uncertainty. Namely, for every . ∈ [0, 1], the alpha-cut .(.) of the desired fuzzy value is the interval tha作者: Reservation 時(shí)間: 2025-3-26 18:44 作者: 相信 時(shí)間: 2025-3-27 00:16 作者: cluster 時(shí)間: 2025-3-27 03:38
Sipho Ngobeni,Hein Venter,Ivan Burkee desired statistical characteristics..To prevent privacy violations, we replace the original values of the quasiidentifier variables with ranges. For example, we divide the set of all possible ages into ranges [0, 10], [10, 20], [20, 30], etc. Then, instead of storing the actual age of 26, we only 作者: 退出可食用 時(shí)間: 2025-3-27 07:49
Hung T. Nguyen,Vladik Kreinovich,Gang XiangRecent advances in Computing Statistics under Interval and Fuzzy Uncertainty.Presents various Applications to Computer Science and Engineering作者: Pepsin 時(shí)間: 2025-3-27 10:02 作者: 我不死扛 時(shí)間: 2025-3-27 16:43
Advances in Digital Forensics II . . . .. In the case of . ., we know the probability distributions for measurement errors corresponding to all the inputs .,..., ., and we want to find the probability distribution corresponding to the statistic .(.,..., .).作者: 狂熱語(yǔ)言 時(shí)間: 2025-3-27 19:22
Benjamin Rodriguez,Gilbert Peterson . .: . .. In the case of interval uncertainty, we only know the intervals, we do not know the probability distributions on these intervals. The traditional statistical approach to situations in which we have several alternatives with unknown probabilities is to use Laplace Principle of Indifference, according to which,作者: Cerumen 時(shí)間: 2025-3-27 22:23
Advances in Digital Forensics III: . .. When the measurement errors Δ. are relatively small, we can use a simplification called .. The main idea of linearization is as follows.作者: HARP 時(shí)間: 2025-3-28 06:03 作者: 熒光 時(shí)間: 2025-3-28 08:43 作者: lacrimal-gland 時(shí)間: 2025-3-28 13:37 作者: 召集 時(shí)間: 2025-3-28 16:18
Towards a Formalization of Digital Forensics . . .: .. In many practical applications, we need to estimate the sample variance . = . · ., where . = . · . ..作者: VOC 時(shí)間: 2025-3-28 19:42 作者: agitate 時(shí)間: 2025-3-29 01:11 作者: anaerobic 時(shí)間: 2025-3-29 03:28
Computing under Interval Uncertainty: When Measurement Errors Are Small: . .. When the measurement errors Δ. are relatively small, we can use a simplification called .. The main idea of linearization is as follows.作者: correspondent 時(shí)間: 2025-3-29 10:05
Computing under Interval Uncertainty: Computational ComplexityIn this chapter, we will briefly describe the computational complexity of the range estimation problem under interval uncertainty.. .. Let us start with the simplest case of a linear function. = .(.,..., .) = . + . .· ...In this case, substituting the (approximate) measured values ., we get the approximate value. = . + . .· .for ..作者: Antigen 時(shí)間: 2025-3-29 14:31 作者: 逃避系列單詞 時(shí)間: 2025-3-29 17:12 作者: 殘酷的地方 時(shí)間: 2025-3-29 23:04
Computing Variance under Interval Uncertainty: An Example of an NP-Hard Problem . . .: .. In many practical applications, we need to estimate the sample variance . = . · ., where . = . · . ..作者: Arthr- 時(shí)間: 2025-3-30 02:16
Computing under Fuzzy Uncertainty Can Be Reduced to Computing under Interval Uncertaintyrst reformulate fuzzy techniques in an interval-related form..In some situations, an expert knows exactly which values of . are possible and which are not. In this situation, the expert’s knowledge can be naturally represented by describing the set of all possible values.作者: 獨(dú)特性 時(shí)間: 2025-3-30 06:21
Computing under Interval Uncertainty: General Algorithmssome applications, it is important to guarantee that the (unknown) actual value . of a certain quantity does not exceed a certain threshold .0. The only way to guarantee this is to have an interval . = [., . ] which is guaranteed to contain . (i.e., for which . ? . ) and for which . ≤ .0.作者: Fabric 時(shí)間: 2025-3-30 09:36 作者: 悅耳 時(shí)間: 2025-3-30 13:30 作者: 施舍 時(shí)間: 2025-3-30 17:03 作者: endocardium 時(shí)間: 2025-3-30 23:02
Computing Statistics under Interval and Fuzzy UncertaintyApplications to Comp作者: Hiatus 時(shí)間: 2025-3-31 02:12
Formulation of the Problemntities characterizing objects from this population. For example, we are interested in the human population in a certain region, and we are interested in their heights, weights, etc..Different objects from a population have, in general, different values of the desired characteristics. Measuring, sto作者: 腐敗 時(shí)間: 2025-3-31 06:44 作者: angiography 時(shí)間: 2025-3-31 10:37
Computing under Fuzzy Uncertainty Can Be Reduced to Computing under Interval Uncertaintyrst reformulate fuzzy techniques in an interval-related form..In some situations, an expert knows exactly which values of . are possible and which are not. In this situation, the expert’s knowledge can be naturally represented by describing the set of all possible values.作者: 辮子帶來(lái)幫助 時(shí)間: 2025-3-31 16:34
Computing under Interval Uncertainty: General Algorithmssome applications, it is important to guarantee that the (unknown) actual value . of a certain quantity does not exceed a certain threshold .0. The only way to guarantee this is to have an interval . = [., . ] which is guaranteed to contain . (i.e., for which . ? . ) and for which . ≤ .0.作者: 珍奇 時(shí)間: 2025-3-31 18:41
Towards Selecting Appropriate Statistical Characteristics: The Basics of Decision Theory and the Notis means, crudely speaking, that it is not possible to design a feasible algorithm that would compute all statistics under interval uncertainty. It is therefore necessary to restrict ourselves to statistical characteristics which are practically useful..Which statistical characteristics should we es作者: grandiose 時(shí)間: 2025-3-31 23:54 作者: characteristic 時(shí)間: 2025-4-1 03:54 作者: Transfusion 時(shí)間: 2025-4-1 07:30
Types of Interval Data Sets: Towards Feasible Algorithmsharacteristics are mean and variance. We already know that computing the mean under interval uncertainty is straightforward. However, as the previous chapter shows, computing variance . under interval uncertainty is, in general, an NP-hard (computationally difficult) problem. As we will see in the f作者: 館長(zhǎng) 時(shí)間: 2025-4-1 11:08
Computing Variance under Interval Uncertainty: Efficient Algorithmsnt . can be always computed in feasible (polynomial) time). Since we cannot always efficiently compute the upper endpoint . , we therefore need to consider cases when such an efficient computation may be possible.作者: Fierce 時(shí)間: 2025-4-1 17:31
Computing Variance under Hierarchical Privacy-Related Interval Uncertaintye desired statistical characteristics..To prevent privacy violations, we replace the original values of the quasiidentifier variables with ranges. For example, we divide the set of all possible ages into ranges [0, 10], [10, 20], [20, 30], etc. Then, instead of storing the actual age of 26, we only
