手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| サポートベクターマシン（分類）× | ナイーブベイズ× | |
|---|---|---|
| 分野 | 機械学習 | 機械学習 |
| 系統 | Machine learning | Machine learning |
| 提唱年≠ | 1995 | 1997 |
| 提唱者≠ | Cortes, C. & Vapnik, V. | Mitchell, T. M. (textbook treatment) |
| 種類≠ | Maximum-margin classifier (kernel method) | Probabilistic classifier (Bayes' theorem with conditional independence) |
| 原典≠ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ | Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072 |
| 別名≠ | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes |
| 関連≠ | 5 | 4 |
| 概要≠ | 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. | Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate. |
| ScholarGateデータセット ↗ |
|
|