Results 21 to 30 of about 6,527,322 (289)

Deep Error-Correcting Output Codes

Algorithms, 2023
Ensemble learning, online learning and deep learning are very effective and versatile in a wide spectrum of problem domains, such as feature extraction, multi-class classification and retrieval.
Li-Na Wang, Hongxu Wei, Yuchen Zheng, Junyu Dong, Guoqiang Zhong   +4 more
doaj   +1 more source

Population-based incremental learning with memory scheme for changing environments [PDF]

, 2005
Copyright @ 2005 ACMIn recent years there has been a growing interest in studying evolutionary algorithms for dynamic optimization problems due to its importance in real world applications.
Yang, S, Shengxiang Yang, Yang, Shengxiang   +2 more
core   +1 more source

Confidence Calibration for Incremental Learning

IEEE Access, 2020
Class incremental learning is an online learning paradigm wherein the classes to be recognized are gradually increased with limited memory, storing only a partial set of examples of past tasks.
Dongmin Kang, Yeonsik Jo, Yeongwoo Nam, Jonghyun Choi   +3 more
doaj   +1 more source

Simplified numerical approach for incremental sheet metal forming process [PDF]

, 2014
The current work presents a finite element approach for numerical simulation of the incremental sheet metal forming (ISF) process, called here ‘‘ISF-SAM’’ (for ISF-Simplified Analysis Modelling).
BATOZ, Jean-Louis, DELAMEZIERE, Arnaud, BEN AYED, Lanouar, NOUARI, Mohammed, ROBERT, Camille   +4 more
core   +1 more source

Class Incremental Learning Method Integrating Balance Weight and Self-supervision [PDF]

Jisuanji kexue yu tansuo
In view of the catastrophic forgetting phenomenon of knowledge in class incremental learning in image classification, the existing class incremental learning methods focus on the correction of the unbalanced offset of the model classification layer ...
GONG Jiayi, XU Xinlei, XIAO Ting, WANG Zhe
doaj   +1 more source

Ensemble of SVMs for Incremental Learning [PDF]

, 2005
Support Vector Machines (SVMs) have been successfully applied to solve a large number of classification and regression problems. However, SVMs suffer from the catastrophic forgetting phenomenon, which results in loss of previously learned information. Learn++ have recently been introduced as an incremental learning algorithm.
Zeki Erdem, Robi Polikar, Fikret S. Gürgen, Nejat Yumusak   +3 more
openaire   +3 more sources

Survey of Federated Incremental Learning [PDF]

Jisuanji kexue
Federated learning,with its unique distributed training mode and secure aggregation mechanism,has become a research hotspot in recent years.However,in real-life scenarios,local model training often faces new data,leading to catastrophic forgetting of old
XIE Jiachen, LIU Bo, LIN Weiwei , ZHENG Jianwen
doaj   +1 more source

On the power of incremental learning

Theoretical Computer Science, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Steffen Lange, Gunter Grieser
openaire   +1 more source

Evaluation and Optimisation of Incremental Processors [PDF]

, 2011
Baumann T, Buß O, Schlangen D. Evaluation and Optimisation of Incremental Processors. Dialogue and Discourse.
Schlangen, David, Schlangen, David ; https://orcid.org/, Buß, Okko, Baumann, Timo, Okko Buß, Timo Baumann   +4 more
core   +1 more source

New Generation Federated Learning

Sensors, 2022
With the development of the Internet of things (IoT), federated learning (FL) has received increasing attention as a distributed machine learning (ML) framework that does not require data exchange. However, current FL frameworks follow an idealized setup
Boyuan Li, Shengbo Chen, Zihao Peng
doaj   +1 more source