Порівняння методів

	Згорткова нейронна мережа (класифікація)	Автокодувальник	Випадковий ліс	Метод опорних векторів (класифікація)
Галузь	Глибоке навчання	Глибоке навчання	Машинне навчання	Машинне навчання
Родина	Machine learning	Machine learning	Machine learning	Machine learning
Рік появи	1998	2006	2001	1995
Автор методу	LeCun, Y. et al.	Hinton, G.E. & Salakhutdinov, R.R.	Breiman, L.	Cortes, C. & Vapnik, V.
Тип	Deep neural network (convolutional)	Neural network (encoder-decoder)	Ensemble (bagging of decision trees)	Maximum-margin classifier (kernel method)
Основоположне джерело	LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11), 2278–2324.	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.
Інші назви	CNN (Evrişimli Sinir Ağı — Sınıflandırma), CNN classification, ConvNet, convolutional network classifier	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
Пов'язані	5	4	4	5
Підсумок	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.	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.