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Wyszukujesz frazę "Pal, Sanghamitra" wg kryterium: Autor


Wyświetlanie 1-3 z 3
Tytuł:
Respondent-specific randomized response technique to estimate sensitive proportion
Autorzy:
Patra, Dipika
Pal, Sanghamitra
Chaudhuri, Arijit
Powiązania:
https://bibliotekanauki.pl/articles/18698401.pdf
Data publikacji:
2023-09-08
Wydawca:
Główny Urząd Statystyczny
Tematy:
protection of privacy
randomized response
sensitive issues
varying probability sampling
Opis:
In estimating the proportion of people bearing a stigmatizing characteristic in a community of people, randomized response techniques are plentifully available in the literature. They are implemented essentially using boxes of similar cards of two distinguishable types. In this paper, we propose a more general procedure using five different types of cards. A respondent-specific randomized response technique is also proposed, in which respondents are allowed to build up the boxes according to their own choices. An immediate objective for this change is to enhance, sense of protection of privacy of the respondents. But as by-products, higher efficiency in terms of actual coverage percentages of confidence intervals and related features are demonstrated by a simulation study, and superior jeopardy levels against divulgence of personal secrecy are also reported to be achievable. AMS subject classification: 62D05.
Źródło:
Statistics in Transition new series; 2023, 24, 4; 53-70
1234-7655
Pojawia się w:
Statistics in Transition new series
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
How privacy may be protected in optional randomized response surveys
Autorzy:
Pal, Sanghamitra
Chaudhuri, Arijit
Patra, Dipika
Powiązania:
https://bibliotekanauki.pl/articles/1363584.pdf
Data publikacji:
2020-06-05
Wydawca:
Główny Urząd Statystyczny
Tematy:
protection of privacy
randomized response
sensitive issues
Warner and other techniques
Opis:
There are materials in literature about how privacy on stigmatizing features like alcoholism, history of tax-evasion, or testing positive in AIDS-related testing may be partially protected by a proper application of randomized response techniques (RRT). The paper demonstrates what amendments are necessary for this approach while applying optional RRTs covering qualitative characteristics, permitting a sampled respondent either to directly reveal sensitive data or choose a randomized response respectively with complementary probabilities. Only a few standard RRTs are illustrated in the text.
Źródło:
Statistics in Transition new series; 2020, 21, 2; 61-87
1234-7655
Pojawia się w:
Statistics in Transition new series
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Survey on multi-objective based parameter optimization for deep learning
Autorzy:
Chakraborty, Mrittika
Pal, Wreetbhas
Bandyopadhyay, Sanghamitra
Maulik, Ujjwal
Powiązania:
https://bibliotekanauki.pl/articles/27312917.pdf
Data publikacji:
2023
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
deep learning
multi-objective optimization
parameter optimization
neural networks
Opis:
Deep learning models form one of the most powerful machine learning models for the extraction of important features. Most of the designs of deep neural models, i.e., the initialization of parameters, are still manually tuned. Hence, obtaining a model with high performance is exceedingly time-consuming and occasionally impossible. Optimizing the parameters of the deep networks, therefore, requires improved optimization algorithms with high convergence rates. The single objective-based optimization methods generally used are mostly time-consuming and do not guarantee optimum performance in all cases. Mathematical optimization problems containing multiple objective functions that must be optimized simultaneously fall under the category of multi-objective optimization sometimes referred to as Pareto optimization. Multi-objective optimization problems form one of the alternatives yet useful options for parameter optimization. However, this domain is a bit less explored. In this survey, we focus on exploring the effectiveness of multi-objective optimization strategies for parameter optimization in conjunction with deep neural networks. The case studies used in this study focus on how the two methods are combined to provide valuable insights into the generation of predictions and analysis in multiple applications.
Źródło:
Computer Science; 2023, 24 (3); 327--359
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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