Results 81 to 90 of about 216,131 (169)

Assessment of an optimal parameter space for spatial cluster detection of SMEAR Estonia flux footprint data using unsupervised learning algorithms

Metsanduslikud Uurimused
Understanding the spatial variability of ecosystem-atmosphere fluxes is essential for accurate carbon and water cycle assessments in forested landscapes.
Noe Steffen M., Thapa Magar Anuj, Graciliano Ferreira Mercuri Emílio   +2 more
doaj   +1 more source

Hyperparameter optimization with approximate gradient

, 2016
Most models in machine learning contain at least one hyperparameter to control for model complexity. Choosing an appropriate set of hyperparameters is both crucial in terms of model accuracy and computationally challenging.
Pedregosa, Fabian
core  

Hyperparameter Learning via Distributional Transfer

, 2018
Bayesian optimisation is a popular technique for hyperparameter learning but typically requires initial exploration even in cases where similar prior tasks have been solved. We propose to transfer information across tasks using learnt representations of training datasets used in those tasks.
Law, HCL, Zhao, P, Chan, L, Huang, J, Sejdinovic, D   +4 more
openaire   +3 more sources

Collaborative hyperparameter tuning

, 2013
Hyperparameter learning has traditionally been a manual task because of the limited number of trials. Today's computing infrastructures allow bigger evaluation budgets, thus opening the way for algorithmic approaches. Recently, surrogate-based optimization was successfully applied to hyperparameter learning for deep belief networks and to WEKA ...
Bardenet, R., Brendel, M., Kégl, Balázs, Sebag, Michèle   +3 more
openaire   +1 more source

Optimized tuberculosis classification system for chest X‐ray images: Fusing hyperparameter tuning with transfer learning approaches

Engineering Reports
Advanced diagnostic methods are necessary for the prompt and reliable identification of tuberculosis (TB), which continues to be a worldwide health problem.
Rakhi Wajgi, Ganesh Yenurkar, Vincent O. Nyangaresi, Badal Wanjari, Sanjana Verma, Arya Deshmukh, Somesh Mallewar   +6 more
doaj   +1 more source

Fairness-Aware Hyperparameter Optimization

, 2020
In recent years, increased usage of machine learning algorithms has been accompanied by several reports of machine bias in areas from recidivism assessment, to job-applicant screening tools, and estimating mortgage default risk. Additionally, recent advances in machine learning have prominently featured so-called "black-box" models (e.g.
openaire   +2 more sources

Investigating Feed-Forward Back-Propagation Neural Network with Different Hyperparameters for Inverse Kinematics of a 2-DoF Robotic Manipulator: A Comparative Study

Chaos Theory and Applications
Inverse kinematics is a significant challenge in robotic manipulators, and finding practical solutions plays a crucial role in achieving precise control.
Rania Bouzid, Jyotindra Narayan, Hassène Gritli   +2 more
doaj   +1 more source

Hyperparameter Selection [PDF]

, 2016
Franck Dernoncourt, Shamim Nemati, Elias Baedorf Kassis, Mohammad Mahdi Ghassemi   +3 more
openaire   +1 more source

Overtuning in Hyperparameter Optimization

Accepted at the Fourth Conference on Automated Machine Learning (Methods Track).
Schneider, Lennart, Bischl, Bernd, Feurer, Matthias   +2 more
openaire   +2 more sources

A flexible framework for hyperparameter optimization using homotopy and surrogate models. [PDF]

Sci Rep
Abraham SJ, Maduranga KDG, Kinnison J, Carmichael Z, Hauenstein JD, Scheirer WJ.   +5 more
europepmc   +1 more source