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Differential replication through copying refers to the process of replicating the decision behavior of a machine learning model using another model that possesses enhanced features and attributes. This process is relevant when external constraints li
Externí odkaz:
http://arxiv.org/abs/2302.02667
Publikováno v:
In Cognitive Systems Research December 2024 88
Regression problems have been widely studied in machinelearning literature resulting in a plethora of regression models and performance measures. However, there are few techniques specially dedicated to solve the problem of how to incorporate categor
Externí odkaz:
http://arxiv.org/abs/2105.13783
When deployed in the wild, machine learning models are usually confronted with data and requirements that constantly vary, either because of changes in the generating distribution or because external constraints change the environment where the model
Externí odkaz:
http://arxiv.org/abs/2007.07981
Akademický článek
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Copies have been proposed as a viable alternative to endow machine learning models with properties and features that adapt them to changing needs. A fundamental step of the copying process is generating an unlabelled set of points to explore the deci
Externí odkaz:
http://arxiv.org/abs/1910.00237
We study model-agnostic copies of machine learning classifiers. We develop the theory behind the problem of copying, highlighting its differences with that of learning, and propose a framework to copy the functionality of any classifier using no prio
Externí odkaz:
http://arxiv.org/abs/1903.01879
In this paper we propose a method to obtain global explanations for trained black-box classifiers by sampling their decision function to learn alternative interpretable models. The envisaged approach provides a unified solution to approximate non-lin
Externí odkaz:
http://arxiv.org/abs/1811.07698
Akademický článek
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Publikováno v:
In Computers & Security September 2017 70:702-722