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04/29/2021
Fast Multiscale Diffusion on Graphs
Diffusing a graph signal at multiple scales requires computing the actio...
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04/24/2020
A survey on domain adaptation theory
All famous machine learning algorithms that correspond to both supervise...
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09/04/2019
Metric Learning from Imbalanced Data
A key element of any machine learning algorithm is the use of a function...
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09/02/2019
An Adjusted Nearest Neighbor Algorithm Maximizing the F-Measure from Imbalanced Data
In this paper, we address the challenging problem of learning from imbal...
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06/14/2019
Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting
We propose a Gradient Boosting algorithm for learning an ensemble of ker...
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03/01/2017
L^3-SVMs: Landmarks-based Linear Local Support Vectors Machines
For their ability to capture non-linearities in the data and to scale to...
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10/14/2016
Theoretical Analysis of Domain Adaptation with Optimal Transport
Domain adaptation (DA) is an important and emerging field of machine lea...
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09/18/2014
Subspace Alignment For Domain Adaptation
In this paper, we introduce a new domain adaptation (DA) algorithm where...
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06/28/2013
A Survey on Metric Learning for Feature Vectors and Structured Data
The need for appropriate ways to measure the distance or similarity betw...
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01/16/2013
Combining Feature and Prototype Pruning by Uncertainty Minimization
We focus in this paper on dataset reduction techniques for use in k-near...
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06/27/2012
