| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNINA9910373927303321 |
|
|
Titolo |
Pattern Recognition Applications and Methods : 8th International Conference, ICPRAM 2019, Prague, Czech Republic, February 19-21, 2019, Revised Selected Papers / / edited by Maria De Marsico, Gabriella Sanniti di Baja, Ana Fred |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
|
|
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
Edizione |
[1st ed. 2020.] |
|
|
|
|
|
Descrizione fisica |
|
1 online resource (XV, 159 p. 132 illus., 53 illus. in color.) |
|
|
|
|
|
|
Collana |
|
Image Processing, Computer Vision, Pattern Recognition, and Graphics ; ; 11996 |
|
|
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
|
|
Soggetti |
|
Pattern recognition |
Optical data processing |
Machine learning |
Computer communication systems |
Mathematical statistics |
Computers |
Pattern Recognition |
Image Processing and Computer Vision |
Machine Learning |
Computer Communication Networks |
Probability and Statistics in Computer Science |
Information Systems and Communication Service |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Nota di contenuto |
|
Theory and Methods -- Applications -- Bayesian Models -- Gaussian Processes -- Neural Networks -- Fuzzy Logic -- Multi-agent Learning -- Natural Language Processing -- Information retrieval -- Web Applications Image-based Modelling. |
|
|
|
|
|
|
|
|
Sommario/riassunto |
|
This book contains revised and extended versions of selected papers from the 8th International Conference on Pattern Recognition, ICPRAM |
|
|
|
|
|
|
|
|
|
|
2019, held in Prague, Czech Republic, in February 2019. The 25 full papers presented together 52 short papers and 32 poster sessions were carefully reviewed and selected from 138 initial submissions. Contributions describing applications of Pattern Recognition techniques to real-world problems, interdisciplinary research, experimental and/or theoretical studies yielding new insights that advance Pattern Recognition methods are especially encouraged. |
|
|
|
|
|
| |