Sampling a dataset for faster analysis and looking at it as a sample from an unknown distribution are two faces of the same coin. We discuss the use of modern techniques involving the Vapnik-Chervonenkis (VC) dimension to study the trade-off between sample size and accuracy of data mining ...
Plotting the distribution of the sample failures over time allows the […] Read more » Tagged censored data transactionalSampling/Data TaaG Analysis – Fast and Easy for Comparing Trends in Large Data Sets Published: May 16, 2016 by Brad Morrow TaaG (trends at a glance) analysis is a...
With the rapid expansion of data, the problem of data imbalance has become increasingly prominent in the fields of medical treatment, finance, network, etc. And it is typically solved using the oversampling method. However, most existing oversampling met
Data miningMicrobial biooceanographyMicrobial ecologyThe ecology and distribution of green phytoplankton (Chlorophyta) in the ocean is poorly known because most studies have focused on groups with large cell size such as diatoms or dinoflagellates that are easily recognized by traditional techniques such ...
直至收敛，或者reward足够好.这样看起来是不是一点不复杂，这里面唯一难一点的就是Beta distribution，看...
Herzog and Rodgers (1988) compared the two modes of data collection across two age levels (under 60 years/60 years of age and older). They found that the older group did not exhibit larger mode differences on response distribution than the younger respondents. In another study, Wilson et al...
This should become clear when we use the random region zoom-in (RRZI; Wang et al. 2014a) method to conduct venue sampling in Sect. 5. A Markov chain is said to be time reversible with respect to stationary distribution \(\pi \) if it satisfies condition \(\pi _ip_{ij}=\pi _jp...
in Figure 1. This example and much of the discussion is based on data generated by the “mixture model.” The mixture model assumes that the data is generated by a mixture of k Gaussian distributions. Each distribution has a corresponding mean and covariance matrix and points are assumed to ...
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distribution in roughness is quite wide at small sample sizes, but quite narrow at the larger sample sizes. This means that the smaller the sampling size, the greater dispersion of joint sample roughness. Therefore, the representativeness of the joint samples is very important. In addition, from...