Results 61 to 70 of about 5,885,991 (322)

Tailoring Self-Supervision for Supervised Learning

, 2022
Accepted to ECCV 2022.
Moon, WonJun, Kim, Ji-Hwan, Heo, Jae-Pil
openaire   +2 more sources

Pseudo-Labeling Optimization Based Ensemble Semi-Supervised Soft Sensor in the Process Industry

Sensors, 2021
Nowadays, soft sensor techniques have become promising solutions for enabling real-time estimation of difficult-to-measure quality variables in industrial processes.
Youwei Li, Huaiping Jin, Shoulong Dong, Biao Yang, Xiangguang Chen   +4 more
doaj   +1 more source

Semi–Supervised vs. Supervised Learning for Mental Health Monitoring: A Case Study on Bipolar Disorder

International Journal of Applied Mathematics and Computer Science, 2023
Acoustic features of speech are promising as objective markers for mental health monitoring. Specialized smartphone apps can gather such acoustic data without disrupting the daily activities of patients.
Casalino Gabriella, Castellano Giovanna, Hryniewicz Olgierd, Leite Daniel, Opara Karol, Radziszewska Weronika, Kaczmarek-Majer Katarzyna   +6 more
doaj   +1 more source

Detecting Cyber Attacks in Smart Grids Using Semi-Supervised Anomaly Detection and Deep Representation Learning

Information, 2021
Smart grids integrate advanced information and communication technologies (ICTs) into traditional power grids for more efficient and resilient power delivery and management, but also introduce new security vulnerabilities that can be exploited by ...
Ruobin Qi, Craig Rasband, Jun Zheng, Raul Longoria   +3 more
doaj   +1 more source

Supervised Classification Problems–Taxonomy of Dimensions and Notation for Problems Identification

IEEE Access, 2021
The paper proposes a taxonomy for categorizing the main features of the supervised learning classification problems and a notation for the identification of the supervised learning classification problem categories.
Ireneusz Czarnowski, Piotr Jedrzejowicz
doaj   +1 more source

A survey on semi-supervised learning

Machine-mediated learning, 2019
Semi-supervised learning is the branch of machine learning concerned with using labelled as well as unlabelled data to perform certain learning tasks.
Jesper E. van Engelen, H. Hoos
semanticscholar   +1 more source

Cardiac Imaging with Electrical Impedance Tomography (EIT) using Multilayer Perceptron Network

Jurnal Elektronika dan Telekomunikasi
This research explores the enhancement of Electrical Impedance Tomography (EIT) for cardiac imaging using Multilayer Perceptron (MLP) networks, focusing on supervised and semi-supervised learning approaches.
Amelia Putri Ristyawardani, Marlin Ramadhan Baidillah, Yudi Adityawarman, Pratondo Busono, Mochamad Adityo Rachmadi, Meta Yantidewi, Endah Rahmawati   +6 more
doaj   +1 more source

Comparing self‐reported race and genetic ancestry for identifying potential differentially methylated sites in endometrial cancer: insights from African ancestry proportions using machine learning models

Molecular Oncology, EarlyView.
Integrating ancestry, differential methylation analysis, and machine learning, we identified robust epigenetic signature genes (ESGs) and Core‐ESGs in Black and White women with endometrial cancer. Core‐ESGs (namely APOBEC1 and PLEKHG5) methylation levels were significantly associated with survival, with tumors from high African ancestry (THA) showing ...
Huma Asif, J. Julie Kim
wiley   +1 more source

CLASSIFICATION BASED ON SEMI-SUPERVISED LEARNING: A REVIEW

Iraqi Journal for Computers and Informatics, 2021
Semi-supervised learning is the class of machine learning that deals with the use of supervised and unsupervised learning to implement the learning process. Conceptually placed between labelled and unlabeled data.
Aska Ezadeen Mehyadin, Adnan Mohsin Abdulazeez   +1 more
doaj   +1 more source

Learning to Learn in a Semi-supervised Fashion [PDF]

, 2020
To address semi-supervised learning from both labeled and unlabeled data, we present a novel meta-learning scheme. We particularly consider that labeled and unlabeled data share disjoint ground truth label sets, which can be seen tasks like in person re-identification or image retrieval.
Chao-Te Chou, Yu-Chiang Frank Wang, Yun-Chun Chen   +2 more
openaire   +3 more sources