If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions.

<p>We compare the performance of the linear-regression-based Metropolis algorithm to a more expressive version that includes quadratic terms. Both versions are evaluated on the same synthetic datasets with a 3-group ground truth. Each algorithm return a set of coarsened <i>variables</...

وصف كامل

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Yuanchen Zhao (12905580) (author)
مؤلفون آخرون:	Otto X. Cordero (7308872) (author), Mikhail Tikhonov (3259908) (author)
منشور في:	2024
الموضوعات:	Evolutionary Biology Ecology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified two proposed previously resource competition framework ensemble quotient optimization detailed microscopic descriptions de novo </ identify useful groupings modeling community function div >< p compare three regression coarse representations predictive use synthetic data group methods offer predictive coarsening coarser representations several approaches recent work prohibitively complex multiple groups microbial ecology manifestly nonlinear key goal groupings even former could empirical success driven way based methods based algorithms approach ).
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions.

مواد مشابهة