Results 81 to 90 of about 1,872,984 (299)

Combining K-fold cross validation with bayesian hyperparameter optimization for accuracy enhancement of land cover and land use classification

Scientific Reports
Land cover and land use (LCLU) information is crucial in different earth observation applications, such as environmental management, infrastructure planning, and urban development.
Pooya Heidari, Asghar Milan
doaj   +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

Score model network: Optimization hyperparameters.

Score model network: Optimization hyperparameters.
Christopher Frederik Blum (17972603), Stefan Harmeling (17972609), Julius Ramakers (17972600), Markus Kollmann (25296), Sabrina König (17972606)   +4 more
core   +1 more source

Robust optimization of SVM hyperparameters in the classification of bioactive compounds [PDF]

, 2015
Background: Support Vector Machine has become one of the most popular machine learning tools used in virtual screening campaigns aimed at finding new drug candidates. Although it can be extremely effective in finding new potentially active compounds, its
Podlewska, Sabina, Czarnecki, Wojciech, Wojciech M Czarnecki, Bojarski, Andrzej J., Sabina Podlewska, Andrzej J Bojarski   +5 more
core   +1 more source

Implantable Ionic Memristors Based on Natural Polymer Heterojunctions

Advanced Functional Materials, EarlyView.
We report an implantable natural polymer‐based ionic memristor composed of hyaluronic acid, chitosan, and PDMS. The device achieved 98.94% accuracy in MNIST classification while reducing training time by 36.8% compared with a conventional artificial neural network (ANN).
Dong‐yup Lee, Byung Ik Park, Gihyeon Yim, Hyunjae Yoo, Sangjun Ma, Jundong Park, Jeong‐Yun Sun   +6 more
wiley   +1 more source

MTMEGPS: An R package for multi-trait and multi-environment genomic and phenomic selection using deep learning

Frontiers in Plant Science
Genomic and phenomic selection have transformed modern breeding by enabling data-driven prediction of complex traits. Deep learning (DL) can further enhance predictive ability by capturing nonlinear patterns that classical and Bayesian approaches often ...
Freddy Mora-Poblete, Javiera Valenzuela-Herrera, Matías Balach, Claudio J. Martínez-Araya, Carlos Maldonado   +4 more
doaj   +1 more source

Bayesian Optimization of Hyperparameters in Machine Learning [PDF]

, 2021
Cilj naše diplomske naloge je bil analizirati Bayesovsko optimizacijo na problemu optimizacije hiperparametrov. Podlaga za analizo sta pogosto uporabljani orodji za optimizacijo hiperparametrov: naključno iskanje in iskanje v mreži.
OCEPEK, DAVID
core  

Multimode Oxide‐Based Optoelectronic Memtransistor for In‐Sensor Vision Processing

Advanced Functional Materials, EarlyView.
A multimode optoelectronic memtransistor (OEMT) is demonstrated for vision explainable artificial intelligence (VXAI) hardware. By integrating optical sensing, electrical masking, and non‐volatile memory, the device enables key operations required for generating saliency information.
Min Gu Lee, Geunyoung Kim, Hwa Young Kim, Do Hoon Kim, Hanchan Song, Huisu Noh, Jungwan Noh, Seungwoo Lee, Sang‐Hee Ko Park, Himchan Cho, Kyung Min Kim   +10 more
wiley   +1 more source

Hyperparameter Optimization for Effort Estimation

CoRR, 2018
Software analytics has been widely used in software engineering for many tasks such as generating effort estimates for software projects. One of the "black arts" of software analytics is tuning the parameters controlling a data mining algorithm. Such hyperparameter optimization has been widely studied in other software analytics domains (e.g.
Tianpei Xia, Rahul Krishna, Jianfeng Chen, George Mathew, Xipeng Shen, Tim Menzies   +5 more
openaire   +3 more sources

Autonomous Multi‐Objective Nanoscale Characterization of Combinatorial (Al, Sc, B)N Films Reveals Composition‐Dependent Ferroelectric Regimes

Advanced Functional Materials, EarlyView.
Autonomous scanning probe microscopy and multi‐objective Bayesian optimization navigate a ternary (Al,Sc,B)N combinatorial library. Registered photoluminescence, electron‐probe compositional mapping, and X‐ray diffraction connect local electromechanical function to defect‐sensitive emission, composition, and crystal structure.
Yu Liu, Aditya Raghavan, Utkarsh Pratiush, Ian Mercer, Pochun Hsieh, Albert Suceava, Chih‐Yu Lee, Edgar Dimitrov, Mauricio Terrones, Ichiro Takeuchi, Venkatraman Gopalan, Jon‐Paul Maria, Sergei V. Kalinin   +12 more
wiley   +1 more source