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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 +3 more
doaj +6 more sources
Distance-Metric Learning for Personalized Survival Analysis [PDF]
Entropy, 2023 Personalized time-to-event or survival prediction with right-censored outcomes is a pervasive challenge in healthcare research. Although various supervised machine learning methods, such as random survival forests or neural networks, have been adapted to
Wolfgang Galetzka +4 more
doaj +2 more sources
Metric Learning in Freewill EEG Pre-Movement and Movement Intention Classification for Brain Machine Interfaces [PDF]
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 +2 more
doaj +2 more sources
Parametric Local Metric Learning for Nearest Neighbor Classification [PDF]
, 2012 We study the problem of learning local metrics for nearest neighbor classification. Most previous works on local metric learning learn a number of local unrelated metrics.
Kalousis, Alexandros +2 more
core +3 more sources
Two-Stage Metric Learning [PDF]
, 2014 In this paper, we present a novel two-stage metric learning algorithm. We first map each learning instance to a probability distribution by computing its similarities to a set of fixed anchor points.
Kalousis, Alexandros +4 more
core +4 more sources
, 2012 We propose a solution to the problem of estimating a Riemannian metric associated with a given differentiable manifold. The metric learning problem is based on minimizing the relative volume of a given set of points. We derive the details for a family of metrics on the multinomial simplex.
Guy Lebanon
openaire +4 more sources
Prediction of drug–disease associations based on reinforcement symmetric metric learning and graph convolution network [PDF]
Frontiers in PharmacologyAccurately identifying novel indications for drugs is crucial in drug research and discovery. Traditional drug discovery is costly and time-consuming.
Huimin Luo +11 more
doaj +2 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
Adaptive Neighborhood Metric Learning [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 In this paper, we reveal that metric learning would suffer from serious inseparable problem if without informative sample mining. Since the inseparable samples are often mixed with hard samples, current informative sample mining strategies used to deal with inseparable problem may bring up some side-effects, such as instability of objective function ...
Jiwen Lu +4 more
openaire +4 more sources
Ground Metric Learning on Graphs [PDF]
Journal of Mathematical Imaging and Vision, 2020 Optimal transport (OT) distances between probability distributions are parameterized by the ground metric they use between observations. Their relevance for real-life applications strongly hinges on whether that ground metric parameter is suitably chosen.
Heitz, Matthieu +4 more
openaire +5 more sources