השוואת שיטות

	חיפוש ארכיטקטורות נוירוניות	מכונת וקטורים תומכים (סיווג)
תחום	למידה עמוקה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	2017	1995
הוגה השיטה	Zoph, B. & Le, Q.V.	Cortes, C. & Vapnik, V.
סוג	Automated architecture optimization (deep learning)	Maximum-margin classifier (kernel method)
מקור מכונן	Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
כינויים	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
קשורות	5	5
תקציר	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.	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.