Results 1 to 10 of about 822,037 (305)

Describing movement learning using metric learning [PDF]

PLoS ONE, 2023
Analysing movement learning can rely on human evaluation, e.g. annotating video recordings, or on computing means in applying metrics on behavioural data.
Antoine Loriette, Wanyu Liu, Frédéric Bevilacqua, Baptiste Caramiaux   +3 more
doaj   +6 more sources

Multiple Cayley-Klein metric learning. [PDF]

PLoS ONE, 2017
As a specific kind of non-Euclidean metric lies in projective space, Cayley-Klein metric has been recently introduced in metric learning to deal with the complex data distributions in computer vision tasks.
Yanhong Bi, Bin Fan, Fuchao Wu
doaj   +4 more sources

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   +2 more sources

Deep Metric Learning via Facility Location

, 2017
Learning the representation and the similarity metric in an end-to-end fashion with deep networks have demonstrated outstanding results for clustering and retrieval.
Jegelka, Stefanie, Murphy, Kevin, Rathod, Vivek, Song, Hyun Oh   +3 more
core   +3 more sources

An Anomaly Node Detection Method for Wireless Sensor Networks Based on Deep Metric Learning with Fusion of Spatial–Temporal Features [PDF]

Sensors
Wireless sensor networks (WSNs) use distributed nodes for tasks such as environmental monitoring and surveillance. The existing anomaly detection methods fail to fully capture correlations in multi-node, multi-modal time series data, limiting their ...
Ziheng Wang, Miao Ye, Jin Cheng, Cheng Zhu, Yong Wang   +4 more
doaj   +2 more sources

Metric Learning in Histopathological Image Classification: Opening the Black Box [PDF]

Sensors, 2023
The application of machine learning techniques to histopathology images enables advances in the field, providing valuable tools that can speed up and facilitate the diagnosis process.
Domenico Amato, Salvatore Calderaro, Giosué Lo Bosco, Riccardo Rizzo, Filippo Vella   +4 more
doaj   +2 more sources

Metric Learning-Guided Semi-Supervised Path-Interaction Fault Diagnosis Method for Extremely Limited Labeled Samples under Variable Working Conditions [PDF]

Sensors, 2023
The lack of labeled data and variable working conditions brings challenges to the application of intelligent fault diagnosis. Given this, extracting labeled information and learning distribution-invariant representation provides a feasible and promising ...
Zheng Yang, Fei Chen, Binbin Xu, Boquan Ma, Zege Qu, Xin Zhou   +5 more
doaj   +2 more sources

CMCS: contrastive-metric learning via vector-level sampling and augmentation for code search. [PDF]

Sci Rep
Song Q, Hu H, Dai T.
europepmc   +3 more sources

Research on the Few-Shot Learning Based on Metrics [PDF]

SHS Web of Conferences, 2022
Deep learning has been rapidly developed and obtained great achievements with a dataintensive condition. However, sufficient datasets are not always available in practical application. In the absence of data, humans can still perform well in studying and
Shen Yican
doaj   +1 more source

Geometric Metric Learning for Multi-Output Learning

Mathematics, 2022
Due to its wide applications, multi-output learning that predicts multiple output values for a single input at the same time is becoming more and more attractive.
Huiping Gao, Zhongchen Ma
doaj   +1 more source