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Created page with "===English=== A machine-learning approach in which an AI system is trained on a dataset that has been labeled by people, usually when there is a specific, known output in mind. #* For example, a model may be trained on millions of pictures explicitly labeled as either ''zebra'' or ''horse''. The AI learns the characteristics of each animal so it can sort new images into those predefined categories. ====Related terms==== [https://en.wiktionary.org/wiki/supervised sup..."
 
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A machine-learning approach in which an AI system is trained on a dataset that has been labeled by people, usually when there is a specific, known output in mind.
A machine-learning approach in which an AI system is trained on a dataset that has been labeled by people, usually when there is a specific, known output in mind.


#* For example, a model may be trained on millions of pictures explicitly labeled as either ''zebra'' or ''horse''. The AI learns the characteristics of each animal so it can sort new images into those predefined categories.
# For example, a model may be trained on millions of pictures explicitly labeled as either ''zebra'' or ''horse''. The AI learns the characteristics of each animal so it can sort new images into those predefined categories.


====Related terms====
====Related terms====


[https://en.wiktionary.org/wiki/supervised
* [https://en.wiktionary.org/wiki/supervised supervised]
supervised]
* [https://en.wiktionary.org/wiki/learning learning]
[https://en.wiktionary.org/wiki/learning
* [https://en.wiktionary.org/wiki/machine_learning machine learning]
learning]
* [https://en.wiktionary.org/wiki/artificial_intelligence artificial intelligence]
[https://en.wiktionary.org/wiki/machine_learning
machine learning]
[https://en.wiktionary.org/wiki/artificial_intelligence
artificial intelligence]

Latest revision as of 19:14, 9 May 2026

English

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A machine-learning approach in which an AI system is trained on a dataset that has been labeled by people, usually when there is a specific, known output in mind.

  1. For example, a model may be trained on millions of pictures explicitly labeled as either zebra or horse. The AI learns the characteristics of each animal so it can sort new images into those predefined categories.
[edit | edit source]