Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Isolation Forest× | Вариационный автокодировщик× | |
|---|---|---|
| Область≠ | Машинное обучение | Глубокое обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2008 | 2014 |
| Автор метода≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. | Kingma, D. P. & Welling, M. |
| Тип≠ | Unsupervised ensemble (random partitioning trees) | Deep generative latent-variable model (encoder–decoder) |
| Основополагающий источник≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ | Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR). link ↗ |
| Другие названия≠ | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection | Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model |
| Связанные | 5 | 5 |
| Сводка≠ | 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. | The Variational Autoencoder (VAE) is a deep generative latent-variable model, introduced by Diederik Kingma and Max Welling in 2014, that encodes data as a probability distribution in a latent space and samples from that distribution to generate new examples. It is used for data generation, anomaly detection, and feature learning. |
| ScholarGateНабор данных ↗ |
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