Now showing items 1-20 of 48

    • Actionable intelligence and online learning for semantic computing 

      Tekin, Cem; van der Schaar, M. (World Scientific Publishing Company, 2017)
      As the world becomes more connected and instrumented, high dimensional, heterogeneous and time-varying data streams are collected and need to be analyzed on the fly to extract the actionable intelligence from the data ...
    • Active learning in context-driven stream mining with an application to ımage mining 

      Tekin, C.; Schaar, Mihaela van der (Institute of Electrical and Electronics Engineers, 2015-11)
      We propose an image stream mining method in which images arrive with contexts (metadata) and need to be processed in real time by the image mining system (IMS), which needs to make predictions and derive actionable ...
    • Adaptive ambulance redeployment via multi-armed bandits 

      Şahin, Ümitcan (Bilkent University, 2019-09)
      Emergency Medical Services (EMS) provide the necessary resources when there is a need for immediate medical attention and play a signi cant role in saving lives in the case of a life-threatening event. Therefore, it is ...
    • Adaptive decision fusion based cooperative spectrum sensing for cognitive radio systems 

      Töreyin, B. U.; Yarkan, S.; Qaraqe, K. A.; Çetin, Ahmet Enis (IEEE, 2011)
      In this paper, an online Adaptive Decision Fusion (ADF) framework is proposed for the central spectrum awareness engine of a spectrum sensor network in Cognitive Radio (CR) systems. Online learning approaches are powerful ...
    • Adaptive ensemble learning with confidence bounds 

      Tekin, C.; Yoon, J.; Schaar, M. V. D. (Institute of Electrical and Electronics Engineers Inc., 2017)
      Extracting actionable intelligence from distributed, heterogeneous, correlated, and high-dimensional data sources requires run-time processing and learning both locally and globally. In the last decade, a large number of ...
    • Asymptotically optimal contextual bandit algorithm using hierarchical structures 

      Neyshabouri, M. M.; Gokcesu, K.; Gokcesu, H.; Ozkan, H.; Kozat, S. S. (Institute of Electrical and Electronics Engineers, 2018)
      We propose an online algorithm for sequential learning in the contextual multiarmed bandit setting. Our approach is to partition the context space and, then, optimally combine all of the possible mappings between the ...
    • Combinatorial multi-armed bandit problem with probabilistically triggered arms: a case with bounded regret 

      Sarıtaç, A. Ömer; Tekin, Cem (IEEE, 2017-11)
      In this paper, we study the combinatorial multi-armed bandit problem (CMAB) with probabilistically triggered arms (PTAs). Under the assumption that the arm triggering probabilities (ATPs) are positive for all arms, we prove ...
    • Computer network intrusion detection using various classifiers and ensemble learning 

      Mirza, Ali H. (IEEE, 2018)
      In this paper, we execute anomaly detection over the computer networks using various machine learning algorithms. We then combine these algorithms to boost the overall performance. We implement three different types of ...
    • Context-aware hierarchical online learning for performance maximization in mobile crowdsourcing 

      Muller, S. K.; Tekin, C.; Schaar, M.; Klein, A. (Institute of Electrical and Electronics Engineers, 2018)
      In mobile crowdsourcing (MCS), mobile users accomplish outsourced human intelligence tasks. MCS requires an appropriate task assignment strategy, since different workers may have different performance in terms of acceptance ...
    • Distributed multi-agent online learning based on global feedback 

      Tekin, C.; Zhang, S.; Schaar, Mihaela van der (Institute of Electrical and Electronics Engineers, 2015-05-01)
      Abstract—In this paper, we develop online learning algorithms that enable the agents to cooperatively learn how to maximize the overall reward in scenarios where only noisy global feedback is available without exchanging ...
    • Distributed online learning via cooperative contextual bandits 

      Tekin, C.; Schaar, Mihaela van der (Institute of Electrical and Electronics Engineers, 2015-07-15)
      In this paper, we propose a novel framework for decentralized, online learning by many learners. At each moment of time, an instance characterized by a certain context may arrive to each learner; based on the context, ...
    • Efficient online learning algorithms based on LSTM neural networks 

      Ergen, T.; Kozat, S. S. (Institute of Electrical and Electronics Engineers, 2018)
      We investigate online nonlinear regression and introduce novel regression structures based on the long short term memory (LSTM) networks. For the introduced structures, we also provide highly efficient and effective online ...
    • Energy consumption forecasting via order preserving pattern matching 

      Vanli, N.D.; Sayin, M.O.; Yildiz H.; Goze, T.; Kozat, S.S. (Institute of Electrical and Electronics Engineers Inc., 2014)
      We study sequential prediction of energy consumption of actual users under a generic loss/utility function. Particularly, we try to determine whether the energy usage of the consumer will increase or decrease in the future, ...
    • Entropy-functional-based online adaptive decision fusion framework with application to wildfire detection in video 

      Gunay, O.; Toreyin, B. U.; Kose, K.; Cetin, A. E. (IEEE, 2012-01-09)
      In this paper, an entropy-functional-based online adaptive decision fusion (EADF) framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm ...
    • eTutor: online learning for personalized education 

      Tekin, Cem; Braun, J.; Schaar, Mihaela van der (IEEE, 2015-04)
      Given recent advances in information technology and artificial intelligence, web-based education systems have became complementary and, in some cases, viable alternatives to traditional classroom teaching. The popularity ...
    • An experimental setup for performance analysis of an online adaptive cooperative spectrum sensing scheme for both in-phase and quadrature branches 

      Yarkan, S.; Qaraqe, K.A.; Töreyin, B.U.; Çetin, A. Enis (IEEE, 2011)
      Spectrum sensing is one of the most essential characteristics of cognitive radios (CRs). Robustness and adaptation to varying wireless propagation scenarios without compromising the sensing accuracy are desirable features ...
    • An experimental validation of an online adaptive cooperation scheme for spectrum sensing 

      Yarkan, S.; Töreyin, B. U.; Qaraqe, K. A.; Çetin, A. Enis (IEEE, 2011-05)
      Cooperative spectrum sensing methods in the literature assume a static communication scenario with fixed channel and propagation environment characteristics. In order to maintain the level of sensing reliability and ...
    • Generalized global bandit and its application in cellular coverage optimization 

      Shen, C.; Zhou, R.; Tekin, C.; Schaar, M. V. D. (Institute of Electrical and Electronics Engineers, 2018)
      Motivated by the engineering problem of cellular coverage optimization, we propose a novel multiarmed bandit model called generalized global bandit. We develop a series of greedy algorithms that have the capability to ...
    • Global bandits 

      Atan, O.; Tekin, C.; Schaar, M. V. D. (Institute of Electrical and Electronics Engineers, 2018)
      Multiarmed bandits (MABs) model sequential decision-making problems, in which a learner sequentially chooses arms with unknown reward distributions in order to maximize its cumulative reward. Most of the prior works on MAB ...
    • 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 ...