Results 11 to 20 of about 7,006,651 (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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Foundations and Trends® in Machine Learning, 2013 The metric learning problem is concerned with learning a distance function tuned to a particular task, and has been shown to be useful when used in conjunction with nearest-neighbor methods and other techniques that rely on distances or similarities. This survey presents an overview of existing research in metric learning, including recent progress on ...
Kulis, Brian
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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
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, 2015 International audience ; Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted
Bellet, Aurélien +2 more
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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
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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
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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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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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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 +4 more
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