Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Isolation Forest× | Variacionālais autoenkoders× | |
|---|---|---|
| Nozare≠ | Mašīnmācīšanās | Dziļā mācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2008 | 2014 |
| Autors≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. | Kingma, D. P. & Welling, M. |
| Tips≠ | Unsupervised ensemble (random partitioning trees) | Deep generative latent-variable model (encoder–decoder) |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi≠ | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection | Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. |
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