השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| יער בידוד× | מודל תערובת גאוסיאנית× | |
|---|---|---|
| תחום | למידת מכונה | למידת מכונה |
| משפחה | Machine learning | Machine learning |
| שנת המקור≠ | 2008 | 1977 |
| הוגה השיטה≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. | Dempster, Laird & Rubin (EM algorithm) |
| סוג≠ | Unsupervised ensemble (random partitioning trees) | Probabilistic (soft) clustering — mixture model |
| מקור מכונן≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ | Dempster, A.P., Laird, N.M. & Rubin, D.B. (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1–22. DOI ↗ |
| כינויים≠ | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection | Gaussian Karışım Modeli (GMM Kümeleme), GMM, GMM clustering, mixture of Gaussians |
| קשורות≠ | 5 | 4 |
| תקציר≠ | Isolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets. | A Gaussian Mixture Model is a probabilistic clustering method that models the data as a weighted mixture of several Gaussian distributions, fitted with the Expectation–Maximization algorithm formalized by Dempster, Laird & Rubin in 1977. It is a generalization of K-means in which each cluster can take its own shape, size, and orientation. |
| ScholarGateמערך נתונים ↗ |
|
|