1.

Record Nr.

UNINA990001754960403321

Autore

Pasquale, Giuseppe Antonio <1820-1893>

Titolo

Note diagnostiche della foglia della Crepis Lacera / Giuseppe Antonio Pasquale

Pubbl/distr/stampa

Napoli : [s.e.], 1876

Descrizione fisica

6 p., 1 tav. ; 29 cm

Disciplina

583.55

Locazione

FAGBC

Collocazione

60 583.32 B 1/22

Lingua di pubblicazione

Italiano

Formato

Materiale a stampa

Livello bibliografico

Monografia

2.

Record Nr.

UNINA9911019699103321

Autore

Albalate Amparo

Titolo

Semi-supervised and unsupervised machine learning : novel strategies / / Amparo Albalate, Wolfgang Minker

Pubbl/distr/stampa

London, : ISTE

Hoboken, N.J., : Wiley, 2011

ISBN

9781118557693

1118557697

9781299139916

1299139914

9781118586334

1118586336

9781118586136

1118586131

Descrizione fisica

1 online resource (320 pages)

Collana

ISTE

Altri autori (Persone)

MinkerWolfgang

Disciplina

6.312

Soggetti

Data mining

Discourse analysis - Statistical methods

Speech processing systems

Computational intelligence



Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

pt. 1. State of the art -- pt. 2. Approaches to semi-supervised classification -- pt. 3. Contributions to unsupervised classification, algorithms to detect the optimal number of clusters.

Sommario/riassunto

This book provides a detailed and up-to-date overview on classification and data mining methods. The first part is focused on supervised classification algorithms and their applications, including recent research on the combination of classifiers. The second part deals with unsupervised data mining and knowledge discovery, with special attention to text mining. Discovering the underlying structure on a data set has been a key research topic associated to unsupervised techniques with multiple applications and challenges, from web-content mining to the inference of cancer subtypes in genomic mic