Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uainishaji wa K-means× | Kikundi cha Kura (Voting Ensemble)× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Mashine | Ujifunzaji wa Mashine |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 1967 (formalized 1982) | 1990s–2004 |
| Mwanzilishi≠ | MacQueen, J. B.; Lloyd, S. P. | Lam & Suen; Kuncheva, L. I. (systematic treatment) |
| Aina≠ | Partitional clustering | Ensemble (combination of multiple classifiers by vote) |
| Chanzo asilia≠ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ | Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8 |
| Majina mbadala | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means | majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble |
| Zinazohusiana≠ | 4 | 5 |
| Muhtasari≠ | 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. | A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted. |
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