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pro vyhledávání: '"Vogel, Robin"'
In practice, and especially when training deep neural networks, visual recognition rules are often learned based on various sources of information. On the other hand, the recent deployment of facial recognition systems with uneven performances on dif
Externí odkaz:
http://arxiv.org/abs/2109.02357
Autor:
Clémençon, Stephan, Vogel, Robin
In multiclass classification, the goal is to learn how to predict a random label $Y$, valued in $\mathcal{Y}=\{1,\; \ldots,\; K \}$ with $K\geq 3$, based upon observing a r.v. $X$, taking its values in $\mathbb{R}^q$ with $q\geq 1$ say, by means of a
Externí odkaz:
http://arxiv.org/abs/2002.09420
Many applications of AI involve scoring individuals using a learned function of their attributes. These predictive risk scores are then used to take decisions based on whether the score exceeds a certain threshold, which may vary depending on the con
Externí odkaz:
http://arxiv.org/abs/2002.08159
We consider statistical learning problems, when the distribution $P'$ of the training observations $Z'_1,\; \ldots,\; Z'_n$ differs from the distribution $P$ involved in the risk one seeks to minimize (referred to as the test distribution) but is sti
Externí odkaz:
http://arxiv.org/abs/2002.05145
Akademický článek
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Autor:
Clémençon, Stéphan, Vogel, Robin
In many situations, the choice of an adequate similarity measure or metric on the feature space dramatically determines the performance of machine learning methods. Building automatically such measures is the specific purpose of metric/similarity lea
Externí odkaz:
http://arxiv.org/abs/1906.09243
The development of cluster computing frameworks has allowed practitioners to scale out various statistical estimation and machine learning algorithms with minimal programming effort. This is especially true for machine learning problems whose objecti
Externí odkaz:
http://arxiv.org/abs/1906.09234
Publikováno v:
PMLR 80 (2018) 5062-5071
The performance of many machine learning techniques depends on the choice of an appropriate similarity or distance measure on the input space. Similarity learning (or metric learning) aims at building such a measure from training data so that observa
Externí odkaz:
http://arxiv.org/abs/1807.06981
Publikováno v:
Journal of Nonparametric Statistics; Sep2024, Vol. 36 Issue 3, p780-803, 24p
Akademický článek
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