পদ্ধতির তুলনা করুন

	নিউরাল আর্কিটেকচার সার্চ	সাপোর্ট ভেক্টর মেশিন (শ্রেণীকরণ)
ক্ষেত্র	গভীর শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2017	1995
প্রবর্তক	Zoph, B. & Le, Q.V.	Cortes, C. & Vapnik, V.
ধরন	Automated architecture optimization (deep learning)	Maximum-margin classifier (kernel method)
মৌলিক উৎস	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.
অপর নাম	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
সম্পর্কিত	5	5
সারসংক্ষেপ	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.