Now showing items 21-40 of 50

    • Highly efficient hierarchical online nonlinear regression using second order methods 

      Civek, B. C.; Delibalta, I.; Kozat, S. S. (Elsevier B.V., 2017)
      We introduce highly efficient online nonlinear regression algorithms that are suitable for real life applications. We process the data in a truly online manner such that no storage is needed, i.e., the data is discarded ...
    • Highly efficient nonlinear regression for big data with lexicographical splitting 

      Neyshabouri, M. M.; Demir, O.; Delibalta, I.; Kozat, S. S. (Springer London, 2017)
      This paper considers the problem of online piecewise linear regression for big data applications. We introduce an algorithm, which sequentially achieves the performance of the best piecewise linear (affine) model with ...
    • Mixture of learners for cancer stem cell detection using CD13 and H and E stained images 

      Oğuz, Oğuzhan; Akbaş, Cem Emre; Mallah, Maen; Taşdemir, K.; Akhan-Güzelcan, E.; Muenzenmayer, C.; Wittenberg, T.; Üner, A.; Çetin, Ahmet Enis; Çetin-Atalay, R. (SPIE, 2016)
      In this article, algorithms for cancer stem cell (CSC) detection in liver cancer tissue images are developed. Conventionally, a pathologist examines of cancer cell morphologies under microscope. Computer aided diagnosis ...
    • Multi-objective contextual bandits with a dominant objective 

      Tekin, Cem; Turgay, Eralp (IEEE, 2017)
      In this paper, we propose a new contextual bandit problem with two objectives, where one of the objectives dominates the other objective. Unlike single-objective bandit problems in which the learner obtains a random scalar ...
    • Multi-objective contextual multi-armed bandit with a dominant objective 

      Tekin, C.; Turgay, E. (Institute of Electrical and Electronics Engineers, 2018)
      We propose a new multi-objective contextual multi-armed bandit (MAB) problem with two objectives, where one of the objectives dominates the other objective. In the proposed problem, the learner obtains a random reward ...
    • Neural networks based online learning 

      Ergen, Tolga; Kozat, Süleyman Serdar (IEEE, 2017)
      In this paper, we investigate online nonlinear regression and introduce novel algorithms based on the long short term memory (LSTM) networks. We first put the underlying architecture in a nonlinear state space form and ...
    • A novel online stacked ensemble for multi-label stream classification 

      Büyükçakır, Alican; Bonab, H.; Can, Fazlı (ACM, 2018)
      As data streams become more prevalent, the necessity for online algorithms that mine this transient and dynamic data becomes clearer. Multi-label data stream classification is a supervised learning problem where each ...
    • An online adaptive cooperation scheme for spectrum sensing based on a second-order statistical method 

      Yarkan S.; Töreyin, B. U.; Qaraqe, K. A.; Çetin, A. E. (Institute of Electrical and Electronics Engineers, 2012)
      Spectrum sensing is one of the most important features of cognitive radio (CR) systems. Although spectrum sensing can be performed by a single CR, it is shown in the literature that cooperative techniques, including multiple ...
    • Online adaptive decision fusion framework based on projections onto convex sets with application to wildfire detection in video 

      Gunay, O.; Toreyin, B. U.; Cetin, A. E. (S P I E - International Society for Optical Engineering, 2011-07-06)
      In this paper, an online adaptive decision fusion framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several sub-algorithms, ...
    • Online additive updates with FFT-IFFT operator on the GRU neural networks 

      Mirza, Ali H. (IEEE, 2018)
      In this paper, we derived the online additive updates of gated recurrent unit (GRU) network by using fast fourier transform-inverse fast fourier transform (FFT-IFFT) operator. In the gating process of the GRU networks, we ...
    • Online anomaly detection in case of limited feedback with accurate distribution learning 

      Marivani, Iman; Kari, Dariush; Kurt, Ali Emirhan; Manış, Eren (IEEE, 2017)
      We propose a high-performance algorithm for sequential anomaly detection. The proposed algorithm sequentially runs over data streams, accurately estimates the nominal distribution using exponential family and then declares ...
    • Online anomaly detection with minimax optimal density estimation in nonstationary environments 

      Gokcesu, K.; Kozat, S. S. (Institute of Electrical and Electronics Engineers, 2018)
      We introduce a truly online anomaly detection algorithm that sequentially processes data to detect anomalies in time series. In anomaly detection, while the anomalous data are arbitrary, the normal data have similarities ...
    • Online churn detection on high dimensional cellular data using adaptive hierarchical trees 

      Khan, Farhan; Delibalta, İ.; Kozat, Süleyman Serdar (IEEE, 2016)
      We study online sequential logistic regression for churn detection in cellular networks when the feature vectors lie in a high dimensional space on a time varying manifold. We escape the curse of dimensionality by tracking ...
    • Online classification via self-organizing space partitioning 

      Ozkan, H.; Vanli, N. D.; Kozat, S. S. (Institute of Electrical and Electronics Engineers Inc., 2016)
      The authors study online supervised learning under the empirical zero-one loss and introduce a novel classification algorithm with strong theoretical guarantees. The proposed method is a highly dynamical self-organizing ...
    • Online density estimation of nonstationary sources using exponential family of distributions 

      Gokcesu, K.; Kozat, S. S. (Institute of Electrical and Electronics Engineers Inc., 2017)
      We investigate online probability density estimation (or learning) of nonstationary (and memoryless) sources using exponential family of distributions. To this end, we introduce a truly sequential algorithm that achieves ...
    • Online distributed nonlinear regression via neural networks 

      Ergen, Tolga; Kozat, Süleyman Serdar (IEEE, 2017)
      In this paper, we study the nonlinear regression problem in a network of nodes and introduce long short term memory (LSTM) based algorithms. In order to learn the parameters of the LSTM architecture in an online manner, ...
    • Online learning in limit order book trade execution 

      Akbarzadeh, Nima; Tekin, Cem; Schaar, M. V. (IEEE, 2018)
      In this paper, we propose an online learning algorithm for optimal execution in the limit order book of a financial asset. Given a certain amount of shares to sell and an allocated time window to complete the transaction, ...
    • Online learning in limit order book trade execution 

      Akbarzadeh, N.; Tekin, C.; van der Schaar, M. (Institute of Electrical and Electronics Engineers, 2018)
      In this paper, we propose an online learning algorithm for optimal execution in the limit order book of a financial asset. Given a certain number of shares to sell and an allocated time window to complete the transaction, ...
    • Online learning over distributed networks 

      Sayın, Muhammed Ömer (Bilkent University, 2015)
      We study online learning strategies over distributed networks. Here, we have a distributed collection of agents with learning and cooperation capabilities. These agents observe a noisy version of a desired state of the ...
    • Online nonlinear modeling for big data applications 

      Khan, Farhan (Bilkent University, 2017-12)
      We investigate online nonlinear learning for several real life, adaptive signal processing and machine learning applications involving big data, and introduce algorithms that are both e cient and e ective. We present ...