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| Halb-überwachtes Gaußsches Mischmodell× | Variationaler Autoencoder× | |
|---|---|---|
| Fachgebiet≠ | Maschinelles Lernen | Deep Learning |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2000 | 2014 |
| Urheber≠ | Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T. | Kingma, D. P. & Welling, M. |
| Typ≠ | Generative semi-supervised classifier | Deep generative latent-variable model (encoder–decoder) |
| Wegweisende Quelle≠ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 | Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR). link ↗ |
| Aliasnamen | SS-GMM, semi-supervised GMM, partially labeled Gaussian mixture model, generative semi-supervised classifier | Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model |
| Verwandt≠ | 3 | 5 |
| Zusammenfassung≠ | The Semi-supervised Gaussian Mixture Model (SS-GMM) is a generative probabilistic classifier that fits a Gaussian mixture to both labeled and unlabeled data using the Expectation-Maximization algorithm. Labeled points constrain component assignments while unlabeled points improve density estimates, enabling effective learning when annotations are scarce. | 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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