Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Random Forest× | Support Vector Machine (Classificazione)× | Transformer (NLP)× | |
|---|---|---|---|
| Campo≠ | Apprendimento automatico | Apprendimento automatico | Apprendimento profondo |
| Famiglia | Machine learning | Machine learning | Machine learning |
| Anno di origine≠ | 2001 | 1995 | 2017 |
| Ideatore≠ | Breiman, L. | Cortes, C. & Vapnik, V. | Vaswani, A. et al. |
| Tipo≠ | Ensemble (bagging of decision trees) | Maximum-margin classifier (kernel method) | Attention-based deep neural network |
| Fonte seminale≠ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ | 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 | 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 | Transformer Modeli (NLP), attention-based language model, self-attention network, transformer NLP |
| Correlati≠ | 4 | 5 | 4 |
| Sintesi≠ | 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. | 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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