Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Giovanni Soda"'
Publikováno v:
RIV Rassegna Italiana di Valutazione. :116-133
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6edba83a91b409f3745351493fb48419
https://doi.org/10.3280/riv2019-075007
https://doi.org/10.3280/riv2019-075007
Publikováno v:
RIV Rassegna Italiana di Valutazione. :173-190
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b86759d829c31b22cf1c4028014611ae
https://doi.org/10.3280/riv2018-071009
https://doi.org/10.3280/riv2018-071009
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence. 28:1187-1199
We propose an approach for the word-level indexing of modern printed documents which are difficult to recognize using current OCR engines. By means of word-level indexing, it is possible to retrieve the position of words in a document, enabling queri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::186f3debf8082a7f4152f292080fa2ee
https://doi.org/10.1109/tpami.2006.162
https://doi.org/10.1109/tpami.2006.162
Publikováno v:
Applied Intelligence. 19:9-25
In this paper we develop novel algorithmic ideas for building a natural language parser grounded upon the hypothesis of incrementality. Although widely accepted and experimentally supported under a cognitive perspective as a model of the human parser
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6d0b022d7d1cf3b60b2093552ffb961a
https://doi.org/10.1023/a:1023860521975
https://doi.org/10.1023/a:1023860521975
Publikováno v:
Journal of Intelligent Information Systems. 18:195-217
In the traditional setting, text categorization is formulated as a concept learning problem where each instance is a single isolated document. However, this perspective is not appropriate in the case of many digital libraries that offer as contents s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ece743474aca619dc7c0b97067f38332
https://doi.org/10.1023/a:1013681528748
https://doi.org/10.1023/a:1013681528748
Autor:
Enrico Appiani, Michelangelo Diligenti, Simone Marinai, Giovanni Soda, Anna Maria Colla, Francesca Cesarini, Marco Gori
Publikováno v:
International Journal on Document Analysis and Recognition. 4:69-83
In this paper a system for analysis and automatic indexing of imaged documents for high-volume applications is described. This system, named STRETCH (STorage and RETrieval by Content of imaged documents), is based on an Archiving and Retrieval Engine
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d94353f615d7d742efe77f1fb00f44f1
https://doi.org/10.1007/pl00010904
https://doi.org/10.1007/pl00010904
Publikováno v:
International Journal on Document Analysis and Recognition. 3:160-168
In this paper we describe the connectionist-based classification engine of an OCR system. The classification engine is based on a new modular connectionist architecture, where a multilayer perceptron (MLP) acting as a classifier is properly combined
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::37a1c687a574f2b4695c1accc12e09b9
https://doi.org/10.1007/pl00013556
https://doi.org/10.1007/pl00013556
Publikováno v:
Pattern Analysis & Applications. 3:182-195
This work presents a methodology for invoice understanding. The invoices of our domain can be grouped into classes according to their logo. The understanding phase is based on two knowledge levels: a specific knowledge for each class, called a docume
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f2cb7062f14d99481cd3171b9ef490c
https://doi.org/10.1007/s100440070022
https://doi.org/10.1007/s100440070022
Publikováno v:
Scopus-Elsevier
Motivation: Predicting the secondary structure of a protein (alpha-helix, beta-sheet, coil) is an important step towards elucidating its three-dimensional structure, as well as its function. Presently, the best predictors are based on machine learnin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b27786d780927f0f62329003c8ac96ef
https://doi.org/10.1093/bioinformatics/15.11.937
https://doi.org/10.1093/bioinformatics/15.11.937
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 11:697-712
We introduce a probabilistic graphical model for supervised learning on databases with categorical attributes. The proposed belief network contains hidden variables that play a role similar to nodes in decision trees and each of their states either c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::92119f8ff510877b125844fceeb4b687
https://doi.org/10.1109/69.806931
https://doi.org/10.1109/69.806931