Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "Pal, Sanghamitra" wg kryterium: Autor

  Lista filtrów
Wyrównaj
    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

      Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies