1.

Record Nr.

UNINA9910557324603321

Autore

Solé-Casals Jordi

Titolo

Machine Learning Methods with Noisy, Incomplete or Small Datasets

Pubbl/distr/stampa

Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021

Descrizione fisica

1 electronic resource (316 p.)

Soggetti

Information technology industries

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Sommario/riassunto

In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios.