Learning in the Absence of Training Data

This book introduces the concept of “bespoke learning”, a new mechanistic approach that makes it possible to generate values of an output variable at each designated value of an associated input variable. Here the output variable generally provides information about the system’s behaviour/structure,...

Popoln opis

Shranjeno v:

Bibliografske podrobnosti
Glavni avtor:	Chakrabarty, Dalia (Author)
Korporativna značnica:	SpringerLink (Online service)
Format:	Elektronski eKnjiga
Jezik:	angleščina
Izdano:	Cham : Springer International Publishing : Imprint: Springer, 2023.
Izdaja:	1st ed. 2023.
Teme:	Statistics . Data mining. Probabilities. Statistical Theory and Methods. Bayesian Inference. Data Mining and Knowledge Discovery. Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. Probability Theory.
Online dostop:	https://doi.org/10.1007/978-3-031-31011-9
Oznake:	Označite Brez oznak, prvi označite!

Kazalo:

1 Bespoke Learning to generate originally-absent training data
2 Forecasting by Learning Evolution-Driver - Application to Forecasting New COVID19 Infections
3 Potential to Density - Application to Learning Galactic Gravitational Mass Density
4 Bespoke Learning in Static Systems - Application to Learning Sub-surface Material Density Function
5 Bespoke Learning of Output using Inter-Network Distance - Application to Haematology-Oncology
A Bayesian inference by posterior sampling using MCMC.

Learning in the Absence of Training Data

Podobne knjige/članki