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| Regularized k-Nearest Neighbors× | Gauß-Prozess× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1967–2000s | 2006 (book); roots in Kriging, 1951) |
| Urheber≠ | Extends Cover & Hart (1967); regularization formulations developed through kernel smoothing literature | Rasmussen, C. E. & Williams, C. K. I. |
| Typ≠ | Instance-based / lazy learner with regularization | Probabilistic non-parametric model |
| Wegweisende Quelle≠ | Cover, T. & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27. DOI ↗ | Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9 |
| Aliasnamen | regularized kNN, kernel-weighted kNN, distance-regularized nearest neighbors, kNN with regularization | GP, Gaussian Process Regression, GPR, Kriging |
| Verwandt≠ | 4 | 3 |
| Zusammenfassung≠ | Regularized k-Nearest Neighbors (kNN) extends the classical nearest-neighbor algorithm by incorporating regularization mechanisms — most commonly kernel-based distance weighting or bandwidth control — that smooth predictions, reduce sensitivity to the choice of k, and lower variance. The result is a more stable and better-calibrated instance-based learner for classification and regression tasks on tabular data. | A Gaussian Process (GP) is a non-parametric, fully probabilistic machine learning model that places a prior distribution directly over functions. Rather than predicting a single value, it returns a predictive mean and a calibrated uncertainty estimate at every test point, making it especially valuable for regression on small to medium datasets and for Bayesian optimization tasks. |
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