Σύγκριση μεθόδων
Εξετάστε τις επιλεγμένες μεθόδους δίπλα-δίπλα· οι γραμμές που διαφέρουν επισημαίνονται.
| Αναζήτηση Νευρωνικής Αρχιτεκτονικής× | Μηχανή Υποστήριξης Διανυσμάτων (Ταξινόμηση)× | |
|---|---|---|
| Πεδίο≠ | Βαθιά Μάθηση | Μηχανική Μάθηση |
| Οικογένεια | Machine learning | Machine learning |
| Έτος προέλευσης≠ | 2017 | 1995 |
| Δημιουργός≠ | Zoph, B. & Le, Q.V. | Cortes, C. & Vapnik, V. |
| Τύπος≠ | Automated architecture optimization (deep learning) | Maximum-margin classifier (kernel method) |
| Θεμελιώδης πηγή≠ | Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR. link ↗ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ |
| Εναλλακτικές ονομασίες | Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| Συναφείς | 5 | 5 |
| Σύνοψη≠ | 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. |
| ScholarGateΣύνολο δεδομένων ↗ |
|
|