Not all mistakes are equal

Name: Not all mistakes are equal
Author: Şensoy, M., Saleki, Maryam, Julier, S., Aydoğan, Reyhan, Reid, J.

İsim	Not all mistakes are equal
Yazar	Şensoy, M., Saleki, Maryam, Julier, S., Aydoğan, Reyhan, Reid, J.
Basım Tarihi:	2020
Basım Yeri	- The ACM Digital Library
Konu	Cost-sensitive learning, Deep learning, Risk, Uncertainty
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-145037518-4
Kayıt Numarası	5f32a648-8b02-4e02-beb0-ef4ce491f1a3
Lokasyon	Computer Science
Tarih	2020
Örnek Metin	In many tasks, classifiers play a fundamental role in the way an agent behaves. Most rational agents collect sensor data from the environment, classify it, and act based on that classification. Recently, deep neural networks (DNNs) have become the dominant approach to develop classifiers due to their excellent performance. When training and evaluating the performance of DNNs, it is normally assumed that the cost of all misclassification errors are equal. However, this is unlikely to be true in practice. Incorrect classification predictions can cause an agent to take inappropriate actions. The costs of these actions can be asymmetric, vary from agent-to-agent, and depend on context. In this paper, we discuss the importance of considering risk and uncertainty quantification together to reduce agents' cost of making misclassifications using deep classifiers.
Cilt	2020

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

Not all mistakes are equal

Yazar Şensoy, M., Saleki, Maryam, Julier, S., Aydoğan, Reyhan, Reid, J.

Basım Tarihi 2020

Basım Yeri - The ACM Digital Library

Konu Cost-sensitive learning, Deep learning, Risk, Uncertainty

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-145037518-4

Kayıt Numarası 5f32a648-8b02-4e02-beb0-ef4ce491f1a3

Lokasyon Computer Science

Tarih 2020

Örnek Metin In many tasks, classifiers play a fundamental role in the way an agent behaves. Most rational agents collect sensor data from the environment, classify it, and act based on that classification. Recently, deep neural networks (DNNs) have become the dominant approach to develop classifiers due to their excellent performance. When training and evaluating the performance of DNNs, it is normally assumed that the cost of all misclassification errors are equal. However, this is unlikely to be true in practice. Incorrect classification predictions can cause an agent to take inappropriate actions. The costs of these actions can be asymmetric, vary from agent-to-agent, and depend on context. In this paper, we discuss the importance of considering risk and uncertainty quantification together to reduce agents' cost of making misclassifications using deep classifiers.

Cilt 2020