Filtreler

2012 - 20153
Filtreleri Sıfırla

Ayarlar

Bölüm: Işık University, Faculty of Engineering, Department of Computer Engineering × Konu: Computer Science ×

Arama Sonuçları

Listeleniyor 1 - 3 / 3
  • Küçük Resim
    Yayın
    A hybrid approach to private record matching
    (IEEE Computer Soc, 2012-10) İnan, Ali; Kantarcıoğlu, Murat; Ghinita, Gabriel; Bertino, Elisa
    Real-world entities are not always represented by the same set of features in different data sets. Therefore, matching records of the same real-world entity distributed across these data sets is a challenging task. If the data sets contain private information, the problem becomes even more difficult. Existing solutions to this problem generally follow two approaches: sanitization techniques and cryptographic techniques. We propose a hybrid technique that combines these two approaches and enables users to trade off between privacy, accuracy, and cost. Our main contribution is the use of a blocking phase that operates over sanitized data to filter out in a privacy-preserving manner pairs of records that do not satisfy the matching condition. We also provide a formal definition of privacy and prove that the participants of our protocols learn nothing other than their share of the result and what can be inferred from their share of the result, their input and sanitized views of the input data sets (which are considered public information). Our method incurs considerably lower costs than cryptographic techniques and yields significantly more accurate matching results compared to sanitization techniques, even when privacy requirements are high.
  • Küçük Resim
    Yayın
    Univariate decision tree induction using maximum margin classification
    (Oxford Univ Press, 2012-03) Yıldız, Olcay Taner
    In many pattern recognition applications, first decision trees are used due to their simplicity and easily interpretable nature. In this paper, we propose a new decision tree learning algorithm called univariate margin tree where, for each continuous attribute, the best split is found using convex optimization. Our simulation results on 47 data sets show that the novel margin tree classifier performs at least as good as C4.5 and linear discriminant tree (LDT) with a similar time complexity. For two-class data sets, it generates significantly smaller trees than C4.5 and LDT without sacrificing from accuracy, and generates significantly more accurate trees than C4.5 and LDT for multiclass data sets with one-vs-rest methodology.
  • Küçük Resim
    Yayın
    Chunking in Turkish with conditional random fields
    (Springer-Verlag, 2015-04-14) Yıldız, Olcay Taner; Solak, Ercan; Ehsani, Razieh; Görgün, Onur
    In this paper, we report our work on chunking in Turkish. We used the data that we generated by manually translating a subset of the Penn Treebank. We exploited the already available tags in the trees to automatically identify and label chunks in their Turkish translations. We used conditional random fields (CRF) to train a model over the annotated data. We report our results on different levels of chunk resolution.