So sánh phương pháp

	Mạng nơ-ron tích chập (Phân loại)	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	1998	2001	1995	2017
Người khởi xướng	LeCun, Y. et al.	Breiman, L.	Cortes, C. & Vapnik, V.	Vaswani, A. et al.
Loại	Deep neural network (convolutional)	Ensemble (bagging of decision trees)	Maximum-margin classifier (kernel method)	Attention-based deep neural network
Công trình gốc	LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11), 2278–2324.	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	CNN (Evrişimli Sinir Ağı — Sınıflandırma), CNN classification, ConvNet, convolutional network classifier	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	5	4	5	4
Tóm tắt	A Convolutional Neural Network (CNN) is a deep learning model, established by LeCun and colleagues in 1998, that learns local patterns directly from images and structured data to classify them. Stacks of convolutional filters discover increasingly abstract features, so manual feature engineering can be largely reduced.	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.