Zobrazeno 1 - 10 of 19 pro vyhledávání: '"Agostino Di Ciaccio"'
1
Deep Learning to Jointly Analyze Images and Clinical Data for Disease Detection
Autor: Agostino Di Ciaccio, Federica Crobu
Publikováno v: Statistical Learning and Modeling in Data Analysis ISBN: 9783030699437
In recent years, computer-assisted diagnostic systems increasingly gained interest through the use of deep learning techniques. Surely, the medical field could be one of the best environments in which the power of the AI algorithms can be tangible fo
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f1319de24928b94e66f1553fc0b3863e
http://hdl.handle.net/11573/1582615
Zobrazit plný text záznamu
2
Trends in Data Mining in 2020
Autor: Agostino Di Ciaccio
Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in e
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::20f1f173738e17322d5ccc926438db4a
Zobrazit plný text záznamu
5
Measuring the prediction error. A comparison of cross-validation, bootstrap and covariance penalty methods
Autor: Agostino Di Ciaccio, Simone Borra
Publikováno v: Computational Statistics & Data Analysis. 54:2976-2989
The estimators most widely used to evaluate the prediction error of a non-linear regression model are examined. An extensive simulation approach allowed the comparison of the performance of these estimators for different non-parametric methods, and w
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0121d37374c814b177505977a2ae1ed0
https://doi.org/10.1016/j.csda.2010.03.004
Zobrazit plný text záznamu
6
Elektronická kniha
Advanced Statistical Methods for the Analysis of Large Data-Sets
Autor: Agostino Di Ciaccio, Mauro Coli, Jose Miguel Angulo Ibanez
The theme of the meeting was “Statistical Methods for the Analysis of Large Data-Sets”. In recent years there has been increasing interest in this subject; in fact a huge quantity of information is often available but standard statistical techniq
Zobrazit plný text záznamu
7
Advanced Statistical Methods for the Analysis of Large Data-Sets
Autor: Agostino Di Ciaccio, Mauro Coli, Jose Miguel Angulo Ibanez
Publikováno v: Advanced Statistical Methods for the Analysis of Large Data-Sets
The theme of the meeting was Statistical Methods for the Analysis of Large Data-Sets. In recent years there has been increasing interest in this subject; in fact a huge quantity of information is often available but standard statistical techniques ar
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1fba0e82330fbf50a9d49c3ab10f2fc9
https://doi.org/10.1007/978-3-642-21037-2
Zobrazit plný text záznamu
9
Bootstrap and Nonparametric Predictors to Impute Missing Data
Autor: Agostino Di Ciaccio
Publikováno v: Classification and Multivariate Analysis for Complex Data Structures ISBN: 9783642133114
A new nonparametric technique to impute missing data is proposed in order to obtain a completed data-matrix, capable of producing a degree of reliability for the imputations. Without taking into account strong assumptions, we introduce multiple imput
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a74339d1718d429edc42ffe490c26213
http://hdl.handle.net/11573/198018
Zobrazit plný text záznamu
10
Methods to Compare NonParametric Classifiers and to Select the Predictors
Autor: Agostino Di Ciaccio, Simone Borra
Publikováno v: Studies in Classification, Data Analysis, and Knowledge Organization ISBN: 9783540238096
In this paper we examine some nonparametric evaluation methods to compare the prediction capability of supervised classification models. We show also the importance, in nonparametric models, to eliminate the noise variables with a simple selection pr
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::096bc39fd263a4986891c12586e42fb5
http://hdl.handle.net/11573/201872
Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání

Omezení vyhledávání
Plný text Recenzováno Digitální knihovna AV ČR
Zdroje