Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Mchanganyiko wa Gaussian mtandaoni× | Uainishaji wa K-means× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Mashine | Ujifunzaji wa Mashine |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2000–2009 | 1967 (formalized 1982) |
| Mwanzilishi≠ | Cappé, O. & Moulines, E. (online EM formulation) | MacQueen, J. B.; Lloyd, S. P. |
| Aina≠ | Probabilistic clustering / density estimation (incremental) | Partitional clustering |
| Chanzo asilia≠ | Cappé, O. & Moulines, E. (2009). On-line expectation-maximization algorithm for latent data models. Journal of the Royal Statistical Society: Series B, 71(3), 593–613. DOI ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ |
| Majina mbadala | Online GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means |
| Zinazohusiana≠ | 5 | 4 |
| Muhtasari≠ | Online Gaussian Mixture Model adapts the classic GMM to streaming or large-scale data by replacing full-batch EM with incremental updates — processing one observation or mini-batch at a time and continuously refining component means, covariances, and mixing weights without revisiting the entire dataset. | K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis. |
| ScholarGateSeti ya data ↗ |
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