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A new linear metric learning (LMML) is first proposed to jointly learn adaptive metrics and the optimal classification hyperplanes.
In this work, we propose an end-to-end metric learning framework. Specifically, a new linear metric learning (LMML) is first proposed to jointly learn adaptive ...
In this work, we propose an end-to-end metric learning framework. Specifically, a new linear metric learning (LMML) is first proposed to jointly learn adaptive ...
Joint learning adaptive metric and optimal classification ... - Altmetric
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Joint learning adaptive metric and optimal classification hyperplane · Neural Networks, January 2022 · 10.1016/j.neunet.2022.01.002 · 35114493 · Yidan Wang, Liming ...
In this work, we propose an end-to-end metric learning framework. Specifically, a new linear metric learning (LMML) is first proposed to jointly learn adaptive ...
Joint learning adaptive metric and optimal classification hyperplane · Author ... Metric learning has attracted a lot of interest in classification tasks ...
Joint learning adaptive metric and optimal classification hyperplane. Article. Jan 2022; NEURAL NETWORKS. Yidan Wang · Liming Yang. Metric learning has ...
In machine learning, support vector machines are supervised max-margin models with associated learning algorithms that analyze data for classification and ...
Distance metric learning aims to find an appropriate method to measure similarities between samples. An excellent distance metric can greatly improve the ...
Abstract. Metric learning based methods have attracted extensive in- terests in recommender systems. Current methods take the user-centric way in metric ...