Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Support Vector Machine (Classificazione)× | Transformer (NLP)× | |
|---|---|---|
| Campo≠ | Apprendimento automatico | Apprendimento profondo |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 1995 | 2017 |
| Ideatore≠ | Cortes, C. & Vapnik, V. | Vaswani, A. et al. |
| Tipo≠ | Maximum-margin classifier (kernel method) | Attention-based deep neural network |
| Fonte seminale≠ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ | Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS. link ↗ |
| Alias | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier | Transformer Modeli (NLP), attention-based language model, self-attention network, transformer NLP |
| Correlati≠ | 5 | 4 |
| Sintesi≠ | 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. | The Transformer is an attention-based deep learning model, introduced by Vaswani and colleagues in 2017, that performs text classification, named-entity recognition, and language modelling by letting every token in a sequence attend directly to every other token. It replaced earlier recurrent designs with a self-attention mechanism that processes whole sequences in parallel. |
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