方法对比

	支持向量机（分类）	Transformer (NLP)
领域	机器学习	深度学习
方法族	Machine learning	Machine learning
起源年份	1995	2017
提出者	Cortes, C. & Vapnik, V.	Vaswani, A. et al.
类型	Maximum-margin classifier (kernel method)	Attention-based deep neural network
开创性文献	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.
别名	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier	Transformer Modeli (NLP), attention-based language model, self-attention network, transformer NLP
相关	5	4
摘要	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.	The Transformer is an attention-based deep learning model, introduced by Vaswani and colleagues in 2017, that performs text classification, named-entity recognition, and language modelling by letting every token in a sequence attend directly to every other token. It replaced earlier recurrent designs with a self-attention mechanism that processes whole sequences in parallel.