Publications

2-s2.0-85059815452

Authors: 22/04/2026 03:03:18

Journal: A spatial econometric model of German and Italian farmland prices is estimated to identify the determinants of farmland prices. It explicitly takes spatial dependencies among neighbouring areas into account, not only in form of spatially lagged farmland prices (spatial lag model) but also in form of spatially lagged explanatory variables (spatial Durbin model). Results show that both agricultural and non-agricultural factors are important for explaining farmland prices in both countries. Differences seem to be stronger within the member states than between the countries.

DOI: Germany

Pages: 10.24989/OEGA.JB.26.20

Keywords: 625882027

MATCH_RESULT_STATUS_FAILURE_NO_MATCH

Authors: AREA MIN. 09 - Ingegneria industriale e dell'informazione

Journal: Most of recent advances in the field of face recognition are related to the use of a convolutional neural network (CNN) and the availability of very large scale training datasets. Unfortunately, large scale public datasets are not available to most of the research community, which therefore can hardly compare with big companies. To overcome this drawback, in this work we suggest to use an already trained CNN and we perform a study in order to evaluate the representation capability of its layers. Most of previous face recognition approaches based on deep learning use a CNN self-trained on a very large training set, taking one on the last intermediate layer as a representation and adding a classification layer trained over a set of known face identities to generalize the recognition capability of the CNN to a set of identities outside the training set. The idea is that the representation capabilities of the last one of two layers of a deep trained CNN is higher than traditional handcrafted features. In this work, starting from a CNN trained for face recognition, we study and compare the representation capability of several different layers in CNNs (not only the last ones) showing that they contain more accurate information about the face image than to believe. The proposed system extracts learned features from different layers of a CNN and uses them as a feature vector for a general purpose classifier. Moreover, we study the independence of the different sets of features used and between learned and handcrafted features, showing that they can be exploited to design an effective ensemble. The proposed approach gains noticeable performance both in the FERET datasets, with the highest performance rates published in the literature, and the Labeled Faces in the Wild (LFW) dataset where it achieves good results. The MATLAB source of our best ensemble approach will be freely available at https://www.dei.unipd.it/node/2357 "+Pattern Recognition and Ensemble Classifiers”.

DOI: Italy||Italy||Italy

Keywords: 616497291

WOS.ISTP

Authors: 28/03/2026 07:16:13; 345 E 47TH ST, NEW YORK, NY 10017 USA

Journal: University of Padua||University of Padua||University of Padua

DOI: Italy||Italy||Italy

Volume: Giannico, S; Castaman, N; Ghidoni, S; Pages: Dept Informat Engn||Dept Informat Engn||Dept Informat Engn-2017

Keywords: 620299998

title_year

Authors: AREA MIN. 09 - Ingegneria industriale e dell'informazione

2025/10/29 20:44:04

Authors: Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni::academicField200022350::600; AREA MIN. 09 - Ingegneria industriale e dell'informazione; Ciclo 30

false

Authors: ch

Journal: 199

DOI: Springer

Abstract ▼

Volume: Italy||Italy||Italy Pages: University of Padova||University of Padova||University of Padova-23392337500||53263580700||7004172198

title_year

Authors: Non assegn; AREA MIN. 09 - Ingegneria industriale e dell'informazione; ITA

AREA MIN. 09 – Ingegneria industriale e dell’informazione||AREA MIN. 11 – Scienze storiche, filosofiche, pedagogiche e psicologiche||AREA MIN. 09 – Ingegneria industriale e dell’informazione – 2021-02-18T09:52:29Z

Authors: AREA MIN. 09 - Ingegneria industriale e dell'informazione; AREA MIN. 11 - Scienze storiche, filosofiche, pedagogiche e psicologiche; ITA

Journal: Monica Fedeli, Daniela Mapelli, Carlo Mariconda

1780690154747 – 1780690154747

Authors: AREA MIN. 09 - Ingegneria industriale e dell'informazione; Non assegn; ITA; AUT; DEU

DOI: 04/06/2026 06:21:00

Italy||Italy||Italy||Italy

Authors: ch scopus.description.abstract; scopus.publisher.name; scopus.subject.keywords; scopus.identifier.isbn; scopus.description.allpeopleoriginal

DOI: eng

Volume: 8976580000||10040521600||23392337500||57209318840 Pages: 1700-60000481||60000686||60000481||60000481