Results 11 to 20 of about 210,501 (299)
metric-learn: Metric Learning Algorithms in Python [PDF]
CoRR, 2019 metric-learn is an open source Python package implementing supervised and weakly-supervised distance metric learning algorithms. As part of scikit-learn-contrib, it provides a unified interface compatible with scikit-learn which allows to easily perform cross-validation, model selection, and pipelining with other machine learning estimators.
de Vazelhes, William +4 more
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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 +4 more
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Coordinated Local Metric Learning [PDF]
2015 IEEE International Conference on Computer Vision Workshop (ICCVW), 2015 Mahalanobis metric learning amounts to learning a linear data projection, after which the L2 metric is used to compute distances. To allow more flexible metrics, not restricted to linear projections, local metric learning techniques have been developed.
Saxena, Shreyas, Verbeek, Jakob
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Metric Learning to Rank. [PDF]
, 2010 We study metric learning as a problem of information retrieval. We present a general metric learning algorithm, based on the structural SVM framework, to learn a metric such that rankings of data induced by dis- tance from a query can be optimized against various ranking measures, such as AUC, Precision-at-k, MRR, MAP or NDCG.
Mcfee, Brian, Lanckriet, Gert
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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 +5 more
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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 +3 more
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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
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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 +6 more
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CoRR, 2020 Code and documentation is available at https://www.github.com/KevinMusgrave/pytorch-metric ...
Kevin Musgrave +2 more
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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 +3 more
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