Σύγκριση μεθόδων

	Μηχανή Υποστήριξης Διανυσμάτων (Ταξινόμηση)	Μετασχηματιστής (Επεξεργασία Φυσικής Γλώσσας)
Πεδίο	Μηχανική Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	1995	2017
Δημιουργός	Cortes, C. & Vapnik, V.	Vaswani, A. et al.
Τύπος	Maximum-margin classifier (kernel method)	Attention-based deep neural network
Θεμελιώδης πηγή	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.
Εναλλακτικές ονομασίες	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
Συναφείς	5	4
Σύνοψη	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.