Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| Isolation Forest× | Variational Autoencoder× | |
|---|---|---|
| Fagområde≠ | Maskinlæring | Dyb læring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 2008 | 2014 |
| Ophavsperson≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. | Kingma, D. P. & Welling, M. |
| Type≠ | Unsupervised ensemble (random partitioning trees) | Deep generative latent-variable model (encoder–decoder) |
| Oprindelig kilde≠ | 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 ↗ |
| Aliasser≠ | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection | Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model |
| Relaterede | 5 | 5 |
| Resumé≠ | 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. |
| ScholarGateDatasæt ↗ |
|
|