Zobrazeno 1 - 10
of 67
pro vyhledávání: '"Razzaghi, Talayeh"'
In the growing world of artificial intelligence, federated learning is a distributed learning framework enhanced to preserve the privacy of individuals' data. Federated learning lays the groundwork for collaborative research in areas where the data i
Externí odkaz:
http://arxiv.org/abs/2311.10832
Autor:
Derakhshi, Mohammad, Razzaghi, Talayeh
Publikováno v:
In Expert Systems With Applications 1 September 2024 249 Part C
Autor:
Kolli, Rajasri, Razzaghi, Talayeh, Pierce, Stephanie, Edwards, Rodney K., Maxted, Marta, Parikh, Pavan
Publikováno v:
In AJOG Global Reports November 2023 3(4)
Autor:
Li, Samuel, Razzaghi, Talayeh
In this work, we investigate the importance of ethnicity in colorectal cancer survivability prediction using machine learning techniques and the SEER cancer incidence database. We compare model performances for 2-year survivability prediction and fea
Externí odkaz:
http://arxiv.org/abs/1901.03896
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Sadrfaridpour, Ehsan, Jeereddy, Sandeep, Kennedy, Ken, Luckow, Andre, Razzaghi, Talayeh, Safro, Ilya
The support vector machine is a flexible optimization-based technique widely used for classification problems. In practice, its training part becomes computationally expensive on large-scale data sets because of such reasons as the complexity and num
Externí odkaz:
http://arxiv.org/abs/1611.05487
Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values
This work is motivated by the needs of predictive analytics on healthcare data as represented by Electronic Medical Records. Such data is invariably problematic: noisy, with missing entries, with imbalance in classes of interests, leading to serious
Externí odkaz:
http://arxiv.org/abs/1604.02123
In medical domain, data features often contain missing values. This can create serious bias in the predictive modeling. Typical standard data mining methods often produce poor performance measures. In this paper, we propose a new method to simultaneo
Externí odkaz:
http://arxiv.org/abs/1503.06250
Autor:
Razzaghi, Talayeh, Safro, Ilya
Solving different types of optimization models (including parameters fitting) for support vector machines on large-scale training data is often an expensive computational task. This paper proposes a multilevel algorithmic framework that scales effici
Externí odkaz:
http://arxiv.org/abs/1410.3348
Autor:
Fuqua, Donovan, Razzaghi, Talayeh
Publikováno v:
In Expert Systems With Applications 15 July 2020 150