Results 51 to 60 of about 127,719 (261)

Automatic Termination for Hyperparameter Optimization

, 2021
Bayesian optimization (BO) is a widely popular approach for the hyperparameter optimization (HPO) in machine learning. At its core, BO iteratively evaluates promising configurations until a user-defined budget, such as wall-clock time or number of iterations, is exhausted. While the final performance after tuning heavily depends on the provided budget,
Makarova, Anastasia, Shen, Huibin, Perrone, Valerio, Klein, Aaron, Faddoul, Jean Baptiste, Krause, Andreas, Seeger, Matthias, Archambeau, Cedric   +7 more
openaire   +3 more sources

Predicting Epileptogenic Tubers in Patients With Tuberous Sclerosis Complex Using a Fusion Model Integrating Lesion Network Mapping and Machine Learning

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Accurate localization of epileptogenic tubers (ETs) in patients with tuberous sclerosis complex (TSC) is essential but challenging, as these tubers lack distinct pathological or genetic markers to differentiate them from other cortical tubers.
Tinghong Liu, Qi Wang, Suhui Kuang, Dezhi Cao, Ping Ding, Shaohui Zhang, Haihua Wei, Zhirong Wei, Jinshan Xu, Xinyu Huang, Bing Liu, Shuli Liang   +11 more
wiley   +1 more source

Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimizationb

Journal of Electronic Science and Technology, 2019
Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models.
Jia Wu, Xiu-Yun Chen, Hao Zhang, Li-Dong Xiong, Hang Lei, Si-Hao Deng   +5 more
doaj   +1 more source

Robust optimization of SVM hyper-parameters for spillway type selection

Ain Shams Engineering Journal, 2021
Spillways, which play a vital role in dams, can be built in various types. Although several studies have been conducted on hydraulic calculations of spillways, studies on type selection that require heuristics knowledge were limited.
Enes Gul, Nuh Alpaslan, M. Emin Emiroglu
doaj   +1 more source

Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms [PDF]

, 2012
Many different machine learning algorithms exist; taking into account each algorithm's hyperparameters, there is a staggeringly large number of possible alternatives overall.
Hoos, Holger H., Hutter, Frank, Leyton-Brown, Kevin, Thornton, Chris   +3 more
core   +2 more sources

Efficient Optimization of Echo State Networks for Time Series Datasets

, 2019
Echo State Networks (ESNs) are recurrent neural networks that only train their output layer, thereby precluding the need to backpropagate gradients through time, which leads to significant computational gains.
Gianniotis, Nikos, Maat, Jacob Reinier, Protopapas, Pavlos   +2 more
core   +1 more source

Hyperparameter Optimization via Sequential Uniform Designs

, 2020
Hyperparameter optimization (HPO) plays a central role in the automated machine learning (AutoML). It is a challenging task as the response surfaces of hyperparameters are generally unknown, hence essentially a global optimization problem. This paper reformulates HPO as a computer experiment and proposes a novel sequential uniform design (SeqUD ...
Yang, Zebin, Zhang, Aijun
openaire   +3 more sources

Hopfield Neural Networks for Online Constrained Parameter Estimation With Time‐Varying Dynamics and Disturbances

International Journal of Adaptive Control and Signal Processing, EarlyView.
This paper proposes two projector‐based Hopfield neural network (HNN) estimators for online, constrained parameter estimation under time‐varying data, additive disturbances, and slowly drifting physical parameters. The first is a constraint‐aware HNN that enforces linear equalities and inequalities (via slack neurons) and continuously tracks the ...
Miguel Pedro Silva
wiley   +1 more source

A Genetic Algorithm Based Optimized Convolutional Neural Network for Face Recognition

International Journal of Applied Mathematics and Computer Science, 2023
Face recognition (FR) is one of the most active research areas in the field of computer vision. Convolutional neural networks (CNNs) have been extensively used in this field due to their good efficiency.
Karlupia Namrata, Mahajan Palak, Abrol Pawanesh, Lehana Parveen K.   +3 more
doaj   +1 more source

Gaussian process based model predictive control : a thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Engineering, School of Engineering and Advanced Technology, Massey University, New Zealand [PDF]

, 2017
The performance of using Model Predictive Control (MPC) techniques is highly dependent on a model that is able to accurately represent the dynamical system. The datadriven modelling techniques are usually used as an alternative approach to obtain such
Cao, Gang
core