방법 비교

	캡슐 네트워크	신경망 구조 탐색	랜덤 포레스트	서포트 벡터 머신 (분류)
분야	딥러닝	딥러닝	머신러닝	머신러닝
계열	Machine learning	Machine learning	Machine learning	Machine learning
기원 연도	2017	2017	2001	1995
창시자	Sabour, S., Frosst, N. & Hinton, G. E.	Zoph, B. & Le, Q.V.	Breiman, L.	Cortes, C. & Vapnik, V.
유형	Deep learning architecture (vector capsules with dynamic routing)	Automated architecture optimization (deep learning)	Ensemble (bagging of decision trees)	Maximum-margin classifier (kernel method)
원전	Sabour, S., Frosst, N. & Hinton, G. E. (2017). Dynamic Routing Between Capsules. Advances in Neural Information Processing Systems (NeurIPS).	Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
별칭	Kapsül Ağı (CapsNet), CapsNet, capsule net, dynamic routing network	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search	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
관련	4	5	4	5
요약	A Capsule Network (CapsNet) is a deep learning architecture introduced by Sara Sabour, Nicholas Frosst and Geoffrey Hinton in 2017 that organises neurons as vectors (capsules) rather than scalar activations, so that spatial hierarchy and pose (orientation) information are encoded directly. It was proposed to overcome the fragility of convolutional networks to changes in viewpoint.	Neural Architecture Search (NAS), introduced by Zoph and Le in 2017, automatically optimizes architectural decisions such as a network's depth, width, and connection structure instead of hand-designing them. Leading methods in the field include DARTS, ENAS, and Once-for-All.	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.