Results 1 to 10 of about 2,939,863 (258)

Minimizing Stochastic Complexity with Ridge Regression [PDF]

open access: yesEntropy
We derive a penalty strength criterion for ridge regression using stochastic complexity, which is a refined variant of the minimum description length principle.
Antony Mizzi   +2 more
doaj   +2 more sources

FPGA-Based Implementation of Stochastic Configuration Networks for Regression Prediction

open access: yesSensors, 2020
The implementation of neural network regression prediction based on digital circuits is one of the challenging problems in the field of machine learning and cognitive recognition, and it is also an effective way to relieve the pressure of the Internet in
Yunqi Gao   +4 more
doaj   +3 more sources

Inferring structure and parameters of stochastic reaction networks with logistic regression. [PDF]

open access: yesPLoS ONE
Identifying network structure and estimating reaction parameters remain central challenges in modeling chemical reaction networks. In this work, we develop likelihood-based methods that use multinomial logistic regression to infer both stoichiometries ...
Boseung Choi   +2 more
doaj   +2 more sources

Evaluation of Mutual Information and Feature Selection for SARS-CoV-2 Respiratory Infection

open access: yesBioengineering, 2023
This study aims to develop a predictive model for SARS-CoV-2 using machine-learning techniques and to explore various feature selection methods to enhance the accuracy of predictions. A precise forecast of the SARS-CoV-2 respiratory infections spread can
Sekar Kidambi Raju   +6 more
doaj   +1 more source

Estimation of Stationary Stochastic Coefficient Regression Models [PDF]

open access: yesThe Egyptian Statistical Journal, 1972
In this paper an attempt is made to estimate a regression equation using a time series of cross-sections. It is assumed that the stochastic coefficient regression vector is distributed across individuals with the same mean and the same variance ...
Farid E. Abdel-Badie   +1 more
doaj   +1 more source

Granular Elastic Network Regression with Stochastic Gradient Descent

open access: yesMathematics, 2022
Linear regression is the use of linear functions to model the relationship between a dependent variable and one or more independent variables. Linear regression models have been widely used in various fields such as finance, industry, and medicine.
Linjie He   +3 more
doaj   +1 more source

A New Lifetime Model, Stochastic Orders and Kidney Infection Regression Model [PDF]

open access: yesJournal of Sciences, Islamic Republic of Iran, 2021
We introduce a method to generate a new class of lifetime models based on the bounded distributions such that the defined models are exclusively a special case of the new class.
Araf Khanjari Idenak   +2 more
doaj   +1 more source

Periodic Fluctuations in the Incidence of Gastrointestinal Cancer

open access: yesFrontiers in Oncology, 2021
PurposeNative stem cells can be periodically replaced during short and long epigenetic intervals. Cancer-prone new stem cells might bring about periodic (non-stochastic) carcinogenic events rather than stochastic events.
Mun-Gan Rhyu   +5 more
doaj   +1 more source

Generalized Stochastic Restricted LARS Algorithm

open access: yesRuhuna Journal of Science, 2022
The Least Absolute Shrinkage and Selection Operator (LASSO) is used to tackle both the multicollinearity issue and the variable selection concurrently in the linear regression model.
Manickavasagar Kayanan   +1 more
doaj   +1 more source

Modeling and Calibration for Some Stochastic Differential Models

open access: yesFractal and Fractional, 2022
In many scientific fields, the dynamics of the system are often known, and the main challenge is to estimate the parameters that model the behavior of the system.
Abdelmalik Moujahid, Fernando Vadillo
doaj   +1 more source

Home - About - Disclaimer - Privacy