Results 1 to 10 of about 210,501 (299)
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 +3 more sources
Lifelong Metric Learning [PDF]
IEEE Transactions on Cybernetics, 2019 The state-of-the-art online learning approaches are only capable of learning the metric for predefined tasks. In this paper, we consider a lifelong learning problem to mimic "human learning," i.e., endowing a new capability to the learned metric for a new task from new online samples and incorporating the previous experiences.
Gan Sun +5 more
core +4 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
Feature Fusion and Metric Learning Network for Zero-Shot Sketch-Based Image Retrieval [PDF]
Entropy, 2023 Zero-shot sketch-based image retrieval (ZS-SBIR) is an important computer vision problem. The image category in the test phase is a new category that was not visible in the training stage.
Honggang Zhao, Mingyue Liu, Mingyong Li
doaj +2 more sources
Robustness and generalization for metric learning [PDF]
Neurocomputing, 2015 Metric learning has attracted a lot of interest over the last decade, but the generalization ability of such methods has not been thoroughly studied. In this paper, we introduce an adaptation of the notion of algorithmic robustness (previously introduced by Xu and Mannor) that can be used to derive generalization bounds for metric learning.
Aurélien Bellet, Amaury Habrard
exaly +4 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 ...
Kun Song 0001 +4 more
openaire +3 more sources
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
CoRR, 2022 The DARPA Lifelong Learning Machines (L2M) program seeks to yield advances in artificial intelligence (AI) systems so that they are capable of learning (and improving) continuously, leveraging data on one task to improve performance on another, and doing so in a computationally sustainable way.
Alexander New +3 more
openaire +2 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 +1 more source