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| FastText× | Naiv Bayes× | |
|---|---|---|
| Ämnesområde≠ | Djupinlärning | Maskininlärning |
| Familj | Machine learning | Machine learning |
| Ursprungsår≠ | 2016 | 1997 |
| Upphovsperson≠ | Joulin, A.; Bojanowski, P.; Grave, E.; Mikolov, T. (Facebook AI Research) | Mitchell, T. M. (textbook treatment) |
| Typ≠ | Subword embedding model and linear text classifier | Probabilistic classifier (Bayes' theorem with conditional independence) |
| Ursprungskälla≠ | Joulin, A., Grave, E., Bojanowski, P. & Mikolov, T. (2017). Bag of Tricks for Efficient Text Classification. In Proceedings of EACL 2017, Short Papers, pp. 427–431. ACL. DOI ↗ | Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072 |
| Alias | fastText, fast text, subword embedding, character n-gram embedding | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes |
| Närliggande≠ | 2 | 4 |
| Sammanfattning≠ | FastText is a word embedding and text classification framework developed by Facebook AI Research (Joulin, Bojanowski, Grave, and Mikolov, 2016–2017) that represents each word as the sum of its character n-gram vectors, allowing it to construct meaningful representations for unseen and morphologically rich words and to perform near state-of-the-art text classification orders of magnitude faster than deep neural network alternatives. | Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate. |
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