Results 21 to 30 of about 425,331 (287)
Learning from low precision samples
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2021 With advances in edge applications in industry and healthcare, machine learning models are increasingly trained on the edge. However, storage and memory infrastructure at the edge are often primitive, due to cost and real-estate constraints.
Ji In Choi +5 more
doaj +1 more source
Stats, 2022 The first hitting time of a boundary or threshold by the sample path of a stochastic process is the central concept of threshold regression models for survival data analysis.
Mei-Ling Ting Lee, George A. Whitmore
doaj +1 more source
A regularized stochastic configuration network based on weighted mean of vectors for regression [PDF]
PeerJ Computer Science, 2023 The stochastic configuration network (SCN) randomly configures the input weights and biases of hidden layers under a set of inequality constraints to guarantee its universal approximation property.
Yang Wang +4 more
doaj +2 more sources
Modelling daily water temperature from air temperature for the Missouri River [PDF]
PeerJ, 2018 The bio-chemical and physical characteristics of a river are directly affected by water temperature, which thereby affects the overall health of aquatic ecosystems. It is a complex problem to accurately estimate water temperature.
Senlin Zhu +2 more
doaj +2 more sources
Regression Designs in Autoregressive Stochastic Processes
The Annals of Statistics, 1974 This paper extends some recent results on asymptotically optimal sequences of experimental designs for regression problems in stochastic processes. In the regression model $Y(t) = \beta f(t) + X(t), 0 \leqq t \leqq 1$, the constant $\beta$ is to be estimated based on observations of $Y(t)$ and its first $m - 1$ derivatives at each of a set $T_n$ of $n$
Hajek, Jaroslav, Kimeldorf, George
openaire +3 more sources
Wavelets for Nonparametric Stochastic Regression with Mixing Stochastic Process [PDF]
Communications in Statistics - Theory and Methods, 2008 We propose a wavelet based stochastic regression function estimator for the estimation of the regression function for a sequence of mixing stochastic process with a common one-dimensional probability density function. Some asymptotic properties of the proposed estimator are investigated.
H. Doosti, M. Afshari, H. A. Niroumand
openaire +1 more source
Journal of Big Data, 2023 This paper presents a stochastic imputation approach for large datasets using a correlation selection methodology when preferred commercial packages struggle to iterate due to numerical problems. A variable range-based guard rail modification is proposed
Benjamin D. Leiby, Darryl K. Ahner
doaj +1 more source
Stochastic expansions using continuous dictionaries: L\'{e}vy adaptive regression kernels [PDF]
, 2011 This article describes a new class of prior distributions for nonparametric function estimation. The unknown function is modeled as a limit of weighted sums of kernels or generator functions indexed by continuous parameters that control local and global ...
Clyde, Merlise A. +2 more
core +2 more sources
ON THE USING OF THE SHANNON INFORMATION QUANTITY IN THE TASKS ASSOCIATED WITH LINEAR REGRESSION
St. Petersburg Polytechnical University Journal: Physics and Mathematics, 2019 The article discusses the use of the Shannon information quantity (SIQ) in the tasks associated with linear regression. It is shown that the SIQ contained in the response components with respect to stochastic parameters is expressed through the Fisher ...
Pichugin Yury
doaj +1 more source
Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization [PDF]
, 2013 We introduce a proximal version of the stochastic dual coordinate ascent method and show how to accelerate the method using an inner-outer iteration procedure. We analyze the runtime of the framework and obtain rates that improve state-of-the-art results
Shalev-Shwartz, Shai, Zhang, Tong
core +1 more source