Autor: Matwin, S. - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Automated approach to classification of mine-like objects using multiple-aspect sonar images
Autorzy:: Wang, X.
Liu, X.
Japkowicz, N.
Matwin, S.
Powiązania:: https://bibliotekanauki.pl/articles/91790.pdf
Data publikacji:: 2014
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: object
sea bed
multiple side-scan sonar
Dempster-Shafer theory
DS concept
classifier
imbalance
imbalanced problem
multi-instance class
Opis:: In this paper, the detection of mines or other objects on the seabed from multiple side-scan sonar views is considered. Two frameworks are provided for this kind of classification. The first framework is based upon the Dempster–Shafer (DS) concept of fusion from a single-view kernel-based classifier and the second framework is based upon the concepts of multi-instance classifiers. Moreover, we consider the class imbalance problem which is always presents in sonar image recognition. Our experimental results show that both of the presented frameworks can be used in mine-like object classification and the presented methods for multi-instance class imbalanced problem are also effective in such classification.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 2; 133-148
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

Tytuł:: A Multi-Party Scheme for Privacy : Preserving Clustering
Autorzy:: Zhan, J.
Matwin, S.
Chang, L.
Powiązania:: https://bibliotekanauki.pl/articles/93043.pdf
Data publikacji:: 2006
Wydawca:: Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:: privacy
security
clustering
Opis:: Preserving data privacy while conducting data clustering among multiple parties is a demanding problem. We address this challenging problem in the following scenario: without disclosing their private data to each other, multiple parties, each having a private data set, want to collaboratively conduct k-medoids clustering. To tackle this problem, we develop secure protocols for multiple parties to achieve this dual goal. The solution is distributed, i.e., there is no central, trusted party having access to all the data. Instead, we define a protocol using homomorphic encryption and digital envelope techniques to exchange the data while keeping it private.
Źródło:: Studia Informatica : systems and information technology; 2006, 1(7); 217-232
1731-2264
Pojawia się w:: Studia Informatica : systems and information technology
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 3.

Tytuł:: How To Construct Support Vector Machines Without Breaching Privacy
Autorzy:: Zhan, J.
Chang, L.
Matwin, S.
Powiązania:: https://bibliotekanauki.pl/articles/92993.pdf
Data publikacji:: 2006
Wydawca:: Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:: privacy
security
support vector machine (SVM)
Opis:: This paper addresses the problem of data sharing among multiple parties in the following scenario: without disclosing their private data to each other, multiple parties, each having a private data set, want to collaboratively construct support vector machines using a linear, polynomial or sigmoid kernel function. To tackle this problem, we develop a secure protocol for multiple parties to conduct the desired computation. In our solution, multiple parties use homomorphic encryption and digital envelope techniques to exchange the data while keeping it private. All the parties are treated symmetrically: they all participate in the encryption and in the computation involved in learning support vector machines.
Źródło:: Studia Informatica : systems and information technology; 2006, 1(7); 233-244
1731-2264
Pojawia się w:: Studia Informatica : systems and information technology
Dostawca treści:: Biblioteka Nauki

Artykuł

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