منابع مشابه
In-Network Estimation of Frequency Moments
We consider the problem of estimating functions of distributed data using a distributed algorithm over a network. The extant literature on computing functions in distributed networks such as wired and wireless sensor networks and peer-to-peer networks deals with computing linear functions of the distributed data when the alphabet size of the data values is small, O(1). We describe a distributed...
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اندیشمندان و صاحب نظران علوم اجتماعی بر این باورند که مرحله تازه ای در تاریخ جوامع بشری اغاز شده است. ویژگیهای این جامعه نو را می توان پدیده هایی از جمله اقتصاد اطلاعاتی جهانی ، هندسه متغیر شبکه ای، فرهنگ مجاز واقعی ، توسعه حیرت انگیز فناوری های دیجیتال، خدمات پیوسته و نیز فشردگی زمان و مکان برشمرد. از سوی دیگر قدرت به عنوان موضوع اصلی علم سیاست جایگاه مهمی در روابط انسانی دارد، قدرت و بازتولید...
15 صفحه اولComparison of Artificial Neural Network, Decision Tree and Bayesian Network Models in Regional Flood Frequency Analysis using L-moments and Maximum Likelihood Methods in Karkheh and Karun Watersheds
Proper flood discharge forecasting is significant for the design of hydraulic structures, reducing the risk of failure, and minimizing downstream environmental damage. The objective of this study was to investigate the application of machine learning methods in Regional Flood Frequency Analysis (RFFA). To achieve this goal, 18 physiographic, climatic, lithological, and land use parameters were ...
متن کاملFrequency Moments
DEFINITION Consider a stream (i.e., an ordered list) S = a1, a2, . . . , an of elements ai ∈ [m] def = {1, 2, . . . ,m}. For i ∈ [m], its frequency fi is the number of times it occurs in S. The k-th frequency moment Fk of S, for real k > 0, is defined to be Fk(S) = ∑ i∈[m] f k i . Interpreting 0 0 as 0, we also define F0 this way, so that it equals the number of distinct elements in S. Observe ...
متن کاملFrequency Moments in Streams
Estimating f0 Here we describe an algorithm for estimating f0 which merges (and hopefully simplifies) ideas from [1] and [2]. First, assume a hash function h : a → [0, 1] uniformly. Let us define a random variable X = minih(ai). Intuitively, X should be roughly 1/m and therefore 1/X should be a fair estimate of m. This is almost true. In what comes next we make this into an exact statement. Let...
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ژورنال
عنوان ژورنال: International Journal of Advances in Engineering Sciences and Applied Mathematics
سال: 2013
ISSN: 0975-0770,0975-5616
DOI: 10.1007/s12572-013-0078-2