Results 11 to 20 of about 187,533 (264)

Adversarial Metric Learning [PDF]

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018
In the past decades, intensive efforts have been put to design various loss functions and metric forms for metric learning problem. These improvements have shown promising results when the test data is similar to the training data. However, the trained models often fail to produce reliable distances on the ambiguous test pairs due to the different ...
Shuo Chen 0003, Chen Gong 0002, Jian Yang 0003, Xiang Li 0041, Yang Wei 0003, Jun Li 0027   +5 more
openaire   +2 more sources

A Local-to-Global Metric Learning Framework From the Geometric Insight

IEEE Access, 2020
Metric plays a key role in the description of similarity between samples. An appropriate metric for data can well represent their distribution and further promote the performance of learning tasks.
Yaxin Peng, Nijing Zhang, Ying Li, Shihui Ying   +3 more
doaj   +1 more source

Metric Learning in Freewill EEG Pre-Movement and Movement Intention Classification for Brain Machine Interfaces

Frontiers in Human Neuroscience, 2022
Decoding movement related intentions is a key step to implement BMIs. Decoding EEG has been challenging due to its low spatial resolution and signal to noise ratio.
William Plucknett, Luis G. Sanchez Giraldo, Jihye Bae   +2 more
doaj   +1 more source

Deep Multiple Metric Learning for Time Series Classification

IEEE Access, 2021
Effective distance metric plays an important role in time series classification. Metric learning, which aims to learn a data-adaptive distance metric to measure the distance among samples, has achieved promising results on time series classification ...
Zhi Chen, Yongguo Liu, Jiajing Zhu, Yun Zhang, Qiaoqin Li, Rongjiang Jin, Xia He   +6 more
doaj   +1 more source

PyTorch Metric Learning

CoRR, 2020
Code and documentation is available at https://www.github.com/KevinMusgrave/pytorch-metric ...
Kevin Musgrave, Serge J. Belongie, Ser-Nam Lim   +2 more
openaire   +2 more sources

Parameter-free basis allocation for efficient multiple metric learning

Machine Learning: Science and Technology, 2023
Metric learning involves learning a metric function for distance measurement, which plays an important role in improving the performance of classification or similarity-based algorithms.
Dongyeon Kim, Yejin Kan, Seungmin Lee, Gangman Yi   +3 more
doaj   +1 more source

Fault Diagnosis of Rolling Bearing Based on Modified Deep Metric Learning Method

Shock and Vibration, 2021
A novel fault diagnosis method of rolling bearing based on deep metric learning and Yu norm is proposed in this paper, which is called a deep metric learning method based on Yu norm (DMN-Yu).
Zengbing Xu, Xiaojuan Li, Hui Lin, Zhigang Wang, Tao Peng   +4 more
doaj   +1 more source

Kernel Geometric Mean Metric Learning

Applied Sciences, 2023
Geometric mean metric learning (GMML) algorithm is a novel metric learning approach proposed recently. It has many advantages such as unconstrained convex objective function, closed form solution, faster computational speed, and interpretability over ...
Zixin Feng, Teligeng Yun, Yu Zhou, Ruirui Zheng, Jianjun He   +4 more
doaj   +1 more source

Learning Neighborhoods for Metric Learning [PDF]

, 2012
Metric learning methods have been shown to perform well on different learning tasks. Many of them rely on target neighborhood relationships that are computed in the original feature space and remain fixed throughout learning. As a result, the learned metric reflects the original neighborhood relations.
Wang Jun, Woznica Adam, Kalousis Alexandros   +2 more
openaire   +3 more sources

Empirical lecturers’ and students’ satisfaction assessment in e-learning systems based on the usage metrics

REID (Research and Evaluation in Education), 2021
Nowadays, in the pandemic of COVID-19, e-learning systems have been widely used to facilitate teaching and learning processes between lecturers and students.
Sulis Sandiwarno
doaj   +1 more source