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[1] [2] [3] In statistics literature, it is sometimes also called optimal experimental design. [4] The information source is also called teacher or oracle. There are situations in which unlabeled data is abundant but manual labeling is expensive. In such a scenario, learning algorithms can actively query the user/teacher for labels.
Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". [ 2 ]
In computer science, computational learning theory (or just learning theory) is a subfield of artificial intelligence devoted to studying the design and analysis of machine learning algorithms. [ 1 ] Overview
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
Meta-learning [1] [2] is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. As of 2017, the term had not found a standard interpretation, however the main goal is to use such metadata to understand how automatic learning can become flexible in solving learning problems, hence to improve the performance of existing ...
Applications of machine learning (ML) in earth sciences include geological mapping, gas leakage detection and geological feature identification.Machine learning is a subdiscipline of artificial intelligence aimed at developing programs that are able to classify, cluster, identify, and analyze vast and complex data sets without the need for explicit programming to do so. [1]
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