Search alternatives:
algorithms linear » algorithms under (Expand Search), algorithms less (Expand Search), algorithm lennard (Expand Search)
algorithm python » algorithm within (Expand Search), algorithms within (Expand Search), algorithm both (Expand Search)
python function » protein function (Expand Search)
linear function » liver function (Expand Search), link function (Expand Search)
algorithm a » algorithm _ (Expand Search), algorithm b (Expand Search), algorithms _ (Expand Search)
a function » _ function (Expand Search)
algorithms linear » algorithms under (Expand Search), algorithms less (Expand Search), algorithm lennard (Expand Search)
algorithm python » algorithm within (Expand Search), algorithms within (Expand Search), algorithm both (Expand Search)
python function » protein function (Expand Search)
linear function » liver function (Expand Search), link function (Expand Search)
algorithm a » algorithm _ (Expand Search), algorithm b (Expand Search), algorithms _ (Expand Search)
a function » _ function (Expand Search)
-
141
-
142
Values of the fitness function for linear kernel LS-SVM algorithm run on various companies’ datasets.
Published 2023“…<p>Values of the fitness function for linear kernel LS-SVM algorithm run on various companies’ datasets.…”
-
143
-
144
-
145
Boxplot of en-CSA compared to CSA, CGO, HHO, SSA, GTO, WOA, PSO, and TSA using benchmark functions.
Published 2024Subjects: -
146
-
147
Spatial Linear Regression with Covariate Measurement Errors: Inference and Scalable Computation in a Functional Modeling Approach
Published 2023“…This work introduces a theoretically backed-up estimation framework for the spatial linear errors-in-variables model in a functional approach. …”
-
148
-
149
-
150
-
151
-
152
-
153
-
154
-
155
Supervised Machine Learning Algorithms for Predicting Rate Constants of Ozone Reaction with Micropollutants
Published 2022“…In this work, several supervised machine learning (ML) algorithms, including multiple linear regression (MLR), support vector machine with radial basis function kernels (SVM-RBF), decision tree (DT), random forest (RF), and deep neutral network (DNN) methods, were used to develop quantitative structure–property relationship (QSPR) models for the estimation of log <i>k</i><sub>O3</sub>. …”
-
156
-
157
-
158
-
159
-
160