Learning from Both Experts and Data
نویسندگان
چکیده
منابع مشابه
Learning experts' preferences from informetric data
In the field of informetrics, agents are often represented by numeric sequences of non necessarily conforming lengths. There are numerous aggregation techniques of such sequences, e.g., the g-index, the h-index, that may be used to compare the output of pairs of agents. In this paper we address a question whether such impact indices may be used to model experts’ preferences accurately.
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Crowdsourcing services are often used to collect a large amount of labeled data for machine learning. Although they provide us an easy way to get labels at very low cost in a short period, they have serious limitations. One of them is the variable quality of the crowd-generated data. There have been many attempts to increase the reliability of crowd-generated data and the quality of classifiers...
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Building classification models from clinical data often requires labeling examples by human experts. However, it is difficult to obtain a perfect set of labels everyone agrees on because medical data are typically very complicated and it is quite common that different experts have different opinions on the same patient data. A solution that has been recently explored by the research community i...
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ژورنال
عنوان ژورنال: Entropy
سال: 2019
ISSN: 1099-4300
DOI: 10.3390/e21121208