Uporedite metode
Pregledajte izabrane metode jednu pored druge; redovi koji se razlikuju su istaknuti.
| Bajezov Gausov proces× | Gausov proces× | |
|---|---|---|
| Oblast | Mašinsko učenje | Mašinsko učenje |
| Porodica | Machine learning | Machine learning |
| Godina nastanka≠ | 1978–2006 | 2006 (book); roots in Kriging, 1951) |
| Tvorac≠ | O'Hagan, A.; Neal, R. M.; Rasmussen, C. E. & Williams, C. K. I. | Rasmussen, C. E. & Williams, C. K. I. |
| Tip≠ | Probabilistic kernel model | Probabilistic non-parametric model |
| Temeljni izvor | Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9 | Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9 |
| Drugi nazivi | GP regression, GPR, Gaussian process model, GP classifier | GP, Gaussian Process Regression, GPR, Kriging |
| Srodne | 3 | 3 |
| Sažetak≠ | A Bayesian Gaussian Process (GP) places a probability distribution directly over functions, using a kernel to encode similarity between inputs. After observing data, Bayes' rule converts this prior into a posterior that yields not just point predictions but calibrated uncertainty estimates at every new input — making it one of the most principled probabilistic models in machine learning. | 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. |
| ScholarGateSkup podataka ↗ |
|
|