Results 11 to 20 of about 2,257,648 (287)

Effects of Automatic Hyperparameter Tuning on the Performance of Multi‐Variate Deep Learning‐Based Rainfall Nowcasting

open access: yesWater Resources Research, 2023
Rainfall nowcasting has become increasingly important as we move into an era where more and more storms are occurring in many countries as a result of climate change.
Amirmasoud Amini   +2 more
doaj   +1 more source

Minimum weight design of truss structure via force method and Jaya algorithm” [PDF]

open access: yesمهندسی مکانیک شریف, 2021
This research aims to minimize the weight of truss structures using force method formulation as a structural analyzer and Jaya algorithm as an optimizer tool.
A. Barzegari   +2 more
doaj   +1 more source

Plant Disease Detection Using Deep Convolutional Neural Network

open access: yesApplied Sciences, 2022
In this research, we proposed a novel 14-layered deep convolutional neural network (14-DCNN) to detect plant leaf diseases using leaf images. A new dataset was created using various open datasets.
J. Arun Pandian   +5 more
doaj   +1 more source

Partitioning Search Spaces of a Randomized Search [PDF]

open access: yesFundamenta Informaticae, 2009
This paper studies the following question: given an instance of the propositional satisfiability problem, a randomized satisfiability solver, and a cluster of n computers, what is the best way to use the computers to solve the instance? Two approaches, simple distribution and search space partitioning as well as their combinations are investigated both
Niemelä Ilkka   +2 more
openaire   +3 more sources

Generalization of navigation memory in honeybees

open access: yesFrontiers in Behavioral Neuroscience, 2023
Flying insects like the honeybee learn multiple features of the environment for efficient navigation. Here we introduce a novel paradigm in the natural habitat, and ask whether the memory of such features is generalized to novel test conditions. Foraging
Eric Bullinger   +2 more
doaj   +1 more source

Agent-Based Collaborative Random Search for Hyperparameter Tuning and Global Function Optimization

open access: yesSystems, 2023
Hyperparameter optimization is one of the most tedious yet crucial steps in training machine learning models. There are numerous methods for this vital model-building stage, ranging from domain-specific manual tuning guidelines suggested by the oracles ...
Ahmad Esmaeili   +2 more
doaj   +1 more source

Stagnation Detection with Randomized Local Search* [PDF]

open access: yesEvolutionary Computation, 2021
AbstractRecently a mechanism called stagnation detection was proposed that automatically adjusts the mutation rate of evolutionary algorithms when they encounter local optima. The so-called SD-(1+1) EA introduced by Rajabi and Witt (2022) adds stagnation detection to the classical (1+1) EA with standard bit mutation.
Amirhossein Rajabi, Carsten Witt
openaire   +3 more sources

Diffusion–Advection Equations on a Comb: Resetting and Random Search

open access: yesMathematics, 2021
This review addresses issues of various drift–diffusion and inhomogeneous advection problems with and without resetting on comblike structures. Both a Brownian diffusion search with drift and an inhomogeneous advection search on the comb structures are ...
Trifce Sandev   +3 more
doaj   +1 more source

Komparasi Teknik Hyperparameter Optimization pada SVM untuk Permasalahan Klasifikasi dengan Menggunakan Grid Search dan Random Search

open access: yesJournal of Applied Informatics and Computing, 2023
Classification is one of the important tasks in the field of Machine Learning. Classification can be viewed as an Optimization Problem (Optimization Problem) with the aim of finding the best model that can represent the relationship/pattern between data ...
Muhamad Fajri, Aji Primajaya
doaj   +1 more source

Random Hyperplane Search Trees

open access: yesSIAM Journal on Computing, 2009
Summary: A hyperplane search tree is a binary tree used to store a set \(S\) of \(n\) \(d\)-dimensional data points. In a random hyperplane search tree for \(S\), the root represents a hyperplane defined by \(d\) data points drawn uniformly at random from \(S\).
Devroye, L, King, J, McDiarmid, C
openaire   +1 more source

Home - About - Disclaimer - Privacy