Sampling without Replacement in Linear Time

نویسندگان

چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Without-Replacement Sampling for Stochastic Gradient Methods

Stochastic gradient methods for machine learning and optimization problems are usually analyzed assuming data points are sampled with replacement. In contrast, sampling without replacement is far less understood, yet in practice it is very common, often easier to implement, and usually performs better. In this paper, we provide competitive convergence guarantees for without-replacement sampling...

متن کامل

Weighted Sampling Without Replacement from Data Streams

Weighted sampling without replacement has proved to be a very important tool in designing new algorithms. Efraimidis and Spirakis (IPL 2006) presented an algorithm for weighted sampling without replacement from data streams. Their algorithm works under the assumption of precise computations over the interval [0, 1]. Cohen and Kaplan (VLDB 2008) used similar methods for their bottom-k sketches. ...

متن کامل

Accelerating weighted random sampling without replacement

Random sampling from discrete populations is one of the basic primitives in statistical computing. This article briefly introduces weighted and unweighted sampling with and without replacement. The case of weighted sampling without replacement appears to be most difficult to implement efficiently, which might be one reason why the R implementation performs slowly for large problem sizes. This p...

متن کامل

Probability Inequalities for Kernel Embeddings in Sampling without Replacement

The kernel embedding of distributions is a popular machine learning technique to manipulate probability distributions and is an integral part of numerous applications. Its empirical counterpart is an estimate from a finite set of samples from the distribution under consideration. However, for large-scale learning problems the empirical kernel embedding becomes infeasible to compute and approxim...

متن کامل

Lattice Paths, Sampling without Replacement, and the Kernel Method

In this work we consider weighted lattice paths in the quarter plane N0 × N0. The steps are given by (m, n) → (m − 1, n), (m, n) → (m, n − 1) and are weighted as follows: (m, n)→ (m− 1, n) by m/(m + n) and step (m, n)→ (m, n− 1) by n/(m + n). The considered lattice paths are absorbed at lines y = x/t− s/t with t ∈ N and s ∈ N0. We provide explicit formulæ for the sum of the weights of paths, st...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: The Computer Journal

سال: 1985

ISSN: 0010-4620,1460-2067

DOI: 10.1093/comjnl/28.4.412