Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Logic learning machine"'
Publikováno v:
Risk Management Magazine, Vol 19, Iss 1, Pp 26-49 (2024)
This study explores an innovative approach to portfolio optimization, bridging traditional Modern Portfolio Theory (MPT) with advanced Machine Learning techniques. We start by recognizing the significance of Markowitz's model in MPT and quickly proce
Externí odkaz:
https://doaj.org/article/b1e4a1ca608b40719e5f7788b5301042
Publikováno v:
IEEE Access, Vol 10, Pp 76243-76260 (2022)
In the era of Industry 4.0, the use of Artificial Intelligence (AI) is widespread in occupational settings. Since dealing with human safety, explainability and trustworthiness of AI are even more important than achieving high accuracy. eXplainable AI
Externí odkaz:
https://doaj.org/article/d1e7aa4bcace434eaaa320880aa5b033
Publikováno v:
BMC Bioinformatics, Vol 20, Iss S9, Pp 1-13 (2019)
Abstract Background Logic Learning Machine (LLM) is an innovative method of supervised analysis capable of constructing models based on simple and intelligible rules. In this investigation the performance of LLM in classifying patients with cancer wa
Externí odkaz:
https://doaj.org/article/6429674bd13446bca0efff70bd2bab4e
Autor:
Ivan Vaccari, Vanessa Orani, Sara Narteni, Maurizio Mongelli, Melissa Ferretti, Enrico Cambiaso
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030840594
CD-MAKE
CD-MAKE
Artificial Intelligence systems are characterized by always less interactions with humans today, leading to autonomous decision-making processes. In this context, erroneous predictions can have severe consequences. As a solution, we design and develo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53367efe36150aaee56d4e07908ac442
http://hdl.handle.net/11583/2966705
http://hdl.handle.net/11583/2966705
Autor:
Mongelli Maurizio, Orani Vanessa
Publikováno v:
Applied Soft Computing and Communication Networks ISBN: 9789813361720
The stability of the dynamical system is associated with the concept of Region of Attraction (ROA), whose accurate estimation opens the door to multidisciplinary approaches involving control theory and machine learning. The Lyapunov theory provides s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0c59d3dfd06b8f975dbc3340f8db8d95
https://doi.org/10.1007/978-981-33-6173-7_15
https://doi.org/10.1007/978-981-33-6173-7_15
Publikováno v:
Journal of Gambling Studies. 33:1121-1137
Identifying potential risk factors for problem gambling (PG) is of primary importance for planning preventive and therapeutic interventions. We illustrate a new approach based on the combination of standard logistic regression and an innovative metho
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a2d7a5abb23c6743c080c0c315efedac
https://doi.org/10.1007/s10899-017-9679-1
https://doi.org/10.1007/s10899-017-9679-1
Autor:
Giacomo Guaita, Nicoletta Musacchio, Giuseppina T. Russo, Federico Pisani, Rita Zilich, Carlo Giorda, Paola Ponzani, Alberto De Micheli
Publikováno v:
BMJ Open Diabetes Research & Care
IntroductionThe aim of this study was to investigate the factors (clinical, organizational or doctor-related) involved in a timely and effective achievement of metabolic control, with no weight gain, in type 2 diabetes.Research design and MethodsOver
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a216cd904eaf521fe92c37d8e985469a
http://europepmc.org/articles/PMC7490948
http://europepmc.org/articles/PMC7490948
Akademický článek
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Kniha
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Publikováno v:
Health informatics journal (Online) (2018): 1–12. doi:10.1177/1460458216655188
info:cnr-pdr/source/autori:Parodi S., Manneschi C., Verda D., Ferrari E., Muselli M./titolo:Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables/doi:10.1177%2F1460458216655188/rivista:Health informatics journal (Online)/anno:2018/pagina_da:1/pagina_a:12/intervallo_pagine:1–12/volume
Europe PubMed Central
info:cnr-pdr/source/autori:Parodi S., Manneschi C., Verda D., Ferrari E., Muselli M./titolo:Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables/doi:10.1177%2F1460458216655188/rivista:Health informatics journal (Online)/anno:2018/pagina_da:1/pagina_a:12/intervallo_pagine:1–12/volume
Europe PubMed Central
This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin’s lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin’s lymphoma patients included a small
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d75630469c58ebac70b44bc95ae195ec