Browsing by Subject "Relative performance"

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    Comprehensive lower bounds on sequential prediction
    (IEEE, 2014-09) Vanlı, N. Denizcan; Sayın, Muhammed O.; Ergüt, S.; Kozat, Süleyman S.
    We study the problem of sequential prediction of real-valued sequences under the squared error loss function. While refraining from any statistical and structural assumptions on the underlying sequence, we introduce a competitive approach to this problem and compare the performance of a sequential algorithm with respect to the large and continuous class of parametric predictors. We define the performance difference between a sequential algorithm and the best parametric predictor as regret, and introduce a guaranteed worst-case lower bounds to this relative performance measure. In particular, we prove that for any sequential algorithm, there always exists a sequence for which this regret is lower bounded by zero. We then extend this result by showing that the prediction problem can be transformed into a parameter estimation problem if the class of parametric predictors satisfy a certain property, and provide a comprehensive lower bound to this case.
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    ItemOpen Access
    A new approach to search result clustering and labeling
    (Springer, Berlin, Heidelberg, 2011) Türel, Anıl; Can, Fazlı
    Search engines present query results as a long ordered list of web snippets divided into several pages. Post-processing of retrieval results for easier access of desired information is an important research problem. In this paper, we present a novel search result clustering approach to split the long list of documents returned by search engines into meaningfully grouped and labeled clusters. Our method emphasizes clustering quality by using cover coefficient-based and sequential k-means clustering algorithms. A cluster labeling method based on term weighting is also introduced for reflecting cluster contents. In addition, we present a new metric that employs precision and recall to assess the success of cluster labeling. We adopt a comparative strategy to derive the relative performance of the proposed method with respect to two prominent search result clustering methods: Suffix Tree Clustering and Lingo. Experimental results in the publicly available AMBIENT and ODP-239 datasets show that our method can successfully achieve both clustering and labeling tasks. © 2011 Springer-Verlag Berlin Heidelberg.
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    ItemOpen Access
    Yapısal veri belirsizlikleri altında yarışmacı doğrusal MMSE kestirim
    (IEEE, 2014-04) Vanlı, N. Denizcan; Sayın, Muhammed Ö.; Kozat, Süleyman S.
    Bu bildiride, yapısal veri belirsizlikleri altında doğrusal kestirim problemi incelenmektedir. Maliyet fonksiyonu olarak ortalama karesel hata (MSE) düşünülmüştür ve sınırlı belirsizlikler altında gürbüz bir algoritma önerilmiştir. Sunulan yöntem yarışmacı algoritma yapısına sahiptir ve bu yapıya ulaşmak için doğrusal kestiricinin performansı, bilinmeyen veri belirsizliklerine göre ayarlanmış doğrusal enküçük MSE (MMSE) kestiricisinin performansına göreceli olarak tanımlanmıştır.Daha sonra, bu göreceli performans ölçütünü en kötü durumdaki sistem modeline göre enküçülten doğrusal kestirici bulunmuştur. Bu yarışmacı kestiriciyi bulmak için çözülmesi gereken problemin yarı-kesin programlama (SDP) problemi olarak düşünülebileceği gösterilmiştir. Ayrıca, teorik sonuçları izah etmek için sayısal örnekler sunulmuştur.