方法对比
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| 朴素贝叶斯 (Naive Bayes) 是一种快速的概率分类器，它应用贝叶斯定理，同时假设特征在给定类别时是条件独立的× | Word2Vec× | |
|---|---|---|
| 领域≠ | 机器学习 | 文本挖掘 |
| 方法族≠ | Machine learning | Process / pipeline |
| 起源年份≠ | 1997 | 2013 |
| 提出者≠ | Mitchell, T. M. (textbook treatment) | Tomas Mikolov et al. |
| 类型≠ | Probabilistic classifier (Bayes' theorem with conditional independence) | Neural word-embedding model |
| 开创性文献≠ | Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072 | Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. link ↗ |
| 别名≠ | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes | word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri |
| 相关 | 4 | 4 |
| 摘要≠ | 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. | Word2Vec is a neural word-embedding technique introduced by Mikolov and colleagues in 2013 that maps each word in a text corpus to a dense numeric vector. Words that appear in similar contexts end up close together in the vector space, so the embeddings capture semantic similarity that can be measured arithmetically. |
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