Zobrazeno 1 - 10
of 121
pro vyhledávání: '"PAPADOPOULOS, HARRIS"'
This study builds upon our previous work by introducing a refined Inductive Conformal Martingale (ICM) approach for addressing Concept Drift (CD). Specifically, we enhance our previously proposed CAUTIOUS betting function to incorporate multiple dens
Externí odkaz:
http://arxiv.org/abs/2406.15760
Publikováno v:
Information Sciences, Volume 308, Pages 113-124, 2015
Vesicoureteral Reflux (VUR) is a pediatric disorder in which urine flows backwards from the bladder to the upper urinary tract. Its detection is of great importance as it increases the risk of a Urinary Tract Infection, which can then lead to a kidne
Externí odkaz:
http://arxiv.org/abs/2312.11355
Publikováno v:
Neurocomputing, Volume 280, Pages 3-12, 2018
The impressive growth of smartphone devices in combination with the rising ubiquity of using mobile platforms for sensitive applications such as Internet banking, have triggered a rapid increase in mobile malware. In recent literature, many studies e
Externí odkaz:
http://arxiv.org/abs/2312.11559
Autor:
Papadopoulos, Harris
Publikováno v:
Neurocomputing, Volume 107, May 2013
Venn Prediction (VP) is a new machine learning framework for producing well-calibrated probabilistic predictions. In particular it provides well-calibrated lower and upper bounds for the conditional probability of an example belonging to each possibl
Externí odkaz:
http://arxiv.org/abs/2312.09912
Publikováno v:
Neural Networks, Volume 24, Issue 8, Pages 842-851, 2011
This paper proposes an extension to conventional regression Neural Networks (NNs) for replacing the point predictions they produce with prediction intervals that satisfy a required level of confidence. Our approach follows a novel machine learning fr
Externí odkaz:
http://arxiv.org/abs/2312.09606
Autor:
Maltoudoglou, Lysimachos, Paisios, Andreas, Lenc, Ladislav, Martínek, Jiří, Král, Pavel, Papadopoulos, Harris
Publikováno v:
Pattern Recognition, Volume 122, February 2022
We extend our previous work on Inductive Conformal Prediction (ICP) for multi-label text classification and present a novel approach for addressing the computational inefficiency of the Label Powerset (LP) ICP, arrising when dealing with a high numbe
Externí odkaz:
http://arxiv.org/abs/2312.09304
Autor:
Papadopoulos, Harris
Gaussian Process Regression (GPR) is a popular regression method, which unlike most Machine Learning techniques, provides estimates of uncertainty for its predictions. These uncertainty estimates however, are based on the assumption that the model is
Externí odkaz:
http://arxiv.org/abs/2310.15641
Autor:
Papadopoulos, Harris
Publikováno v:
Proceedings of the 3rd Workshop on Conformal Prediction and its Applications (COPA 2014), IFIP AICT 437, pp. 241-250. Springer, 2014
Unlike the typical classification setting where each instance is associated with a single class, in multi-label learning each instance is associated with multiple classes simultaneously. Therefore the learning task in this setting is to predict the s
Externí odkaz:
http://arxiv.org/abs/2211.16238
Akademický článek
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Autor:
Demetriou, Demetris, Mavromatidis, Pavlos, Robert, Ponsian M., Papadopoulos, Harris, Petrou, Michael F., Nicolaides, Demetris
Publikováno v:
In Waste Management 15 July 2023 167:194-203
