Name: Learning in the Absence of Training Data (en Inglés)
Price: 197720.00 CLP
Availability: InStock
Author: Chakrabarty, Dalia
ISBN: 9783031310102

portada Learning in the Absence of Training Data (en Inglés)

Formato

Libro Físico

Autor

Dalia Chakrabarty

Editorial

Springer

Idioma

Inglés

N° páginas

227

Encuadernación

Tapa Dura

Dimensiones

23.4 x 15.6 x 1.6 cm

Peso

0.53 kg.

ISBN13

9783031310102

Categorías

Inferencia bayesiana
Data mining: minería de datos

Learning in the Absence of Training Data (en Inglés)

Dalia Chakrabarty (Autor) · Springer · Tapa Dura

Libro Físico

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Origen: Estados Unidos (Costos de importación incluídos en el precio)

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Reseña del libro "Learning in the Absence of Training Data (en Inglés)"

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, and the aim is to learn the input-output relationship, even though little to no information on the output is available, as in multiple real-world problems. Once the output values have been bespoke-learnt, the originally-absent training set of input-output pairs becomes available, so that (supervised) learning of the sought inter-variable relation is then possible. Three ways of undertaking such bespoke learning are offered: by tapping into system dynamics in generic dynamical systems, to learn the function that causes the system's evolution; by comparing realisations of a random graph variable, given multivariate time series datasets of disparate temporal coverage; and by designing maximally information-availing likelihoods in static systems. These methodologies are applied to four different real-world problems: forecasting daily COVID-19 infection numbers; learning the gravitational mass density in a real galaxy; learning a sub-surface material density function; and predicting the risk of onset of a disease following bone marrow transplants. Primarily aimed at graduate and postgraduate students studying a field which includes facets of statistical learning, the book will also benefit experts working in a wide range of applications. The prerequisites are undergraduate level probability and stochastic processes, and preliminary ideas on Bayesian statistics.