Is Unsupervised Clustering Somehow Truer? Is Unsupervised Clustering Somehow Truer?

Scientists increasingly approach the world through machine learning techniques, but philosophers of science often question their epistemic status. Some philosophers have argued that the use of unsupervised clustering algorithms is more justified than the use of supervised classification, because sup...

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Vydáno v:	Minds and machines (Dordrecht) Ročník 34; číslo 4
Hlavní autor:	Søgaard, Anders
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Dordrecht Springer Netherlands 29.10.2024 Springer Nature B.V
Témata:	Algorithms Artificial Intelligence Classification Clustering Cognitive Psychology Computer Science Economics Game Theory Machine learning Philosophy of Mind Philosophy of Science Social and Behav. Sciences Theory of Computation Epistemic status Supervised classification Unsupervised clustering Overfitting
ISSN:	1572-8641, 0924-6495, 1572-8641
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Abstract	Scientists increasingly approach the world through machine learning techniques, but philosophers of science often question their epistemic status. Some philosophers have argued that the use of unsupervised clustering algorithms is more justified than the use of supervised classification, because supervised classification is more biased, and because (parametric) simplicity plays a different and more interesting role in unsupervised clustering. I call these arguments the No-Bias Argument and the Simplicity-Truth Argument . I show how both arguments are fallacious and how, on the contrary, the use of supervised classification is at least as justified as the use of unsupervised clustering.
AbstractList	Scientists increasingly approach the world through machine learning techniques, but philosophers of science often question their epistemic status. Some philosophers have argued that the use of unsupervised clustering algorithms is more justified than the use of supervised classification, because supervised classification is more biased, and because (parametric) simplicity plays a different and more interesting role in unsupervised clustering. I call these arguments the No-Bias Argument and the Simplicity-Truth Argument . I show how both arguments are fallacious and how, on the contrary, the use of supervised classification is at least as justified as the use of unsupervised clustering. Scientists increasingly approach the world through machine learning techniques, but philosophers of science often question their epistemic status. Some philosophers have argued that the use of unsupervised clustering algorithms is more justified than the use of supervised classification, because supervised classification is more biased, and because (parametric) simplicity plays a different and more interesting role in unsupervised clustering. I call these arguments the No-Bias Argument and the Simplicity-Truth Argument. I show how both arguments are fallacious and how, on the contrary, the use of supervised classification is at least as justified as the use of unsupervised clustering.
ArticleNumber	43
Author	Søgaard, Anders
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SubjectTerms	Algorithms Artificial Intelligence Classification Clustering Cognitive Psychology Computer Science Economics Game Theory Machine learning Philosophy of Mind Philosophy of Science Social and Behav. Sciences Theory of Computation
Subtitle	Is Unsupervised Clustering Somehow Truer?
Title	Is Unsupervised Clustering Somehow Truer?
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