So sánh phương pháp

	Bộ tự mã hóa	Rừng ngẫu nhiên	Máy Vectơ Hỗ trợ (Phân loại)	Transformer (NLP)
Lĩnh vực	Học sâu	Học máy	Học máy	Học sâu
Họ	Machine learning	Machine learning	Machine learning	Machine learning
Năm ra đời	2006	2001	1995	2017
Người khởi xướng	Hinton, G.E. & Salakhutdinov, R.R.	Breiman, L.	Cortes, C. & Vapnik, V.	Vaswani, A. et al.
Loại	Neural network (encoder-decoder)	Ensemble (bagging of decision trees)	Maximum-margin classifier (kernel method)	Attention-based deep neural network
Công trình gốc	Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.
Tên gọi khác	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	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
Liên quan	4	4	5	4
Tóm tắt	An autoencoder is an encoder-decoder neural network, popularised by Hinton and Salakhutdinov in 2006, that compresses data into a low-dimensional latent code and then reconstructs it, enabling dimensionality reduction and anomaly detection. By learning to rebuild its own input through a narrow bottleneck, it discovers a compact representation of the data.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.	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.