“machine learning”的概念、定义、翻译、参考文献-科学参考

machine learning

Mathematics

An interdisciplinary field centred on artificial intelligence with the aim of constructing programs or algorithms that learn from experience, that is, from introduction to further data. See neural nets.

Statistics

An interdisciplinary field concerned with the interpretation of data. For example, the sounds of breaking glass and running feet near a greenhouse might be interpreted as vandalism. With supervised learning the interpretation is informed by a training sample (see neural net) and generally takes the form of ensemble learning wherein the final outcome is reached algorithmically using information from many different models of the data. Examples are bagging, boosting, and the random forest.
In unsupervised learning the aim is to find underlying patterns in the data using techniques such as cluster analysis, the EM algorithm, factor analysis, graphical models, hidden Markov models, multiple regression models, and principal components analysis. See also classification tree.
A key feature is the intention that, whichever approach is used, the accuracy of the computer algorithm will improve over time (the ‘machine’ will ‘learn’) as a result of feedback concerning previous accuracy.

Computer

A branch of artificial intelligence concerned with the construction of programs that learn from experience. Learning may take many forms, ranging from learning from examples and learning by analogy to autonomous learning of concepts and learning by discovery.
Incremental learning involves continuous improvement as new data arrives while one-shot or batch learning distinguishes a training phase from the application phase. Supervised learning occurs when the training input has been explicitly labelled with the classes to be learned.
Most learning methods aim to demonstrate generalization whereby the system develops efficient and effective representations that encompass large chunks of closely related data.

Internet

A set of techniques that are used to make decisions about some entity after processing a series of known examples of that entity. For example, machine learning has been used to discover whether an email is spam based on learning features from some genuine spam emails.

单词	machine learning
释义	machine learning Mathematics An interdisciplinary field centred on artificial intelligence with the aim of constructing programs or algorithms that learn from experience, that is, from introduction to further data. See neural nets. Statistics An interdisciplinary field concerned with the interpretation of data. For example, the sounds of breaking glass and running feet near a greenhouse might be interpreted as vandalism. With supervised learning the interpretation is informed by a training sample (see neural net) and generally takes the form of ensemble learning wherein the final outcome is reached algorithmically using information from many different models of the data. Examples are bagging, boosting, and the random forest. In unsupervised learning the aim is to find underlying patterns in the data using techniques such as cluster analysis, the EM algorithm, factor analysis, graphical models, hidden Markov models, multiple regression models, and principal components analysis. See also classification tree. A key feature is the intention that, whichever approach is used, the accuracy of the computer algorithm will improve over time (the ‘machine’ will ‘learn’) as a result of feedback concerning previous accuracy. Computer A branch of artificial intelligence concerned with the construction of programs that learn from experience. Learning may take many forms, ranging from learning from examples and learning by analogy to autonomous learning of concepts and learning by discovery. Incremental learning involves continuous improvement as new data arrives while one-shot or batch learning distinguishes a training phase from the application phase. Supervised learning occurs when the training input has been explicitly labelled with the classes to be learned. Most learning methods aim to demonstrate generalization whereby the system develops efficient and effective representations that encompass large chunks of closely related data. Internet A set of techniques that are used to make decisions about some entity after processing a series of known examples of that entity. For example, machine learning has been used to discover whether an email is spam based on learning features from some genuine spam emails.
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