Results 31 to 40 of about 58,776 (261)

Class-Incremental Learning Based on Big Dataset Pre-Trained Models

IEEE Access, 2023
Deep neural networks have shown excellent performance in the field of pattern classification and are widely used. However, real-world data are often cannot be obtained at once, and the knowledge of old classes will be heavily forgotten when training new ...
Bin Wen, Qiuyu Zhu
doaj   +1 more source

Class Incremental Learning With Large Domain Shift

IEEE Access
We address an important and practical problem facing deep-learning-based image classification: class incremental learning with a large domain shift. Most previous efforts on class incremental learning focus on one aspect of the problem, i.e., learning to
Kamin Lee, Hyoeun Kim, Geunjae Choi, Hyejeong Jeon, Nojun Kwak   +4 more
doaj   +1 more source

Class-Incremental Learning with Repetition

CoRR, 2023
Accepted to the 2nd Conference on Lifelong Learning Agents (CoLLAs), 2023 19 ...
Hamed Hemati, Andrea Cossu, Antonio Carta, Julio Hurtado, Lorenzo Pellegrini, Davide Bacciu, Vincenzo Lomonaco, Borth Damian   +7 more
openaire   +7 more sources

(Un)supervised (Co)adaptation via Incremental Learning for Myoelectric Control: Motivation, Review, and Future Directions

IEEE Transactions on Neural Systems and Rehabilitation Engineering
This paper presents a narrative review of incremental learning methods for myoelectric control, outlining both the historical trajectory and potential of adaptive prosthetic systems.
Evan Campbell, Fabio Egle, Marius OsWald, Ulysse Cote-Allard, Patrick M. Pilarski, Nicolo Boccardo, Roberto Meattini, Ivan Vujaklija, Levi Hargrove, Michele Canepa, Ethan Eddy, Alessandro Del Vecchio, Claudio Castellini, Erik Scheme   +13 more
doaj   +1 more source

Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning

Sensors, 2021
Recently, the interest in biometric authentication based on electrocardiograms (ECGs) has increased. Nevertheless, the ECG signal of a person may vary according to factors such as the emotional or physical state, thus hindering authentication. We propose
Junmo Kim, Geunbo Yang, Juhyeong Kim, Seungmin Lee, Ko Keun Kim, Cheolsoo Park   +5 more
doaj   +1 more source

Incremental Learning-to-Learn with Statistical Guarantees [PDF]

CoRR, 2018
In learning-to-learn the goal is to infer a learning algorithm that works well on a class of tasks sampled from an unknown meta distribution. In contrast to previous work on batch learning-to-learn, we consider a scenario where tasks are presented sequentially and the algorithm needs to adapt incrementally to improve its performance on future tasks ...
Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil   +3 more
openaire   +2 more sources

Enhancing Case-based Reasoning Approach using Incremental Learning Model for Automatic Adaptation of Classifiers in Mobile Phishing Detection

International Journal of Networked and Distributed Computing (IJNDC), 2020
This article presents the threshold-based incremental learning model for a case-base updating approach that can support adaptive detection and incremental learning of Case-based Reasoning (CBR)-based automatic adaptable phishing detection.
San Kyaw Zaw, Sangsuree Vasupongayya
doaj   +1 more source

Incremental multiclass open-set audio recognition

IJAIN (International Journal of Advances in Intelligent Informatics), 2022
Incremental learning aims to learn new classes if they emerge while maintaining the performance for previously known classes. It acquires useful information from incoming data to update the existing models.
Hitham Jleed, Martin Bouchard
doaj   +1 more source

Incremental multi‐view correlated feature learning based on non‐negative matrix factorisation

IET Computer Vision, 2021
In real‐world applications, large amounts of data from multiple sources come in the form of streams. This makes multi‐view feature learning cost much time when new instances rise incrementally.
Liang Zhao, Tao Yang, Jie Zhang, Zhikui Chen   +3 more
doaj   +1 more source

Single‐cell DNA methylation profiling: Technologies, computation, and applications in precision oncology

Molecular Oncology, EarlyView.
Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley   +1 more source