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Browsing by Subject "Learning algorithms"

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    • Article  

      Analysis of neuromuscular disorders using statistical and entropy metrics on surface EMG 

      Istenic, R.; Kaplanis, P. A.; Pattichis, Constantinos S.; Zazula, D. (2008)
      This paper introduces the surface electromyogram (EMG) classification system based on statistical and entropy metrics. The system is intended for diagnostic use and enables classification of examined subject as normal, ...

    • Article  

      Assessment of stroke risk based on morphological ultrasound image analysis with conformal prediction 

      Lambrou, Alexandra; Papadopoulos, Harris; Kyriacou, Efthyvoulos C.; Pattichis, Constantinos S.; Pattichis, Marios S.; Gammerman, A.; Nicolaïdes, Andrew N. (2010)
      Non-invasive ultrasound imaging of carotid plaques allows for the development of plaque image analysis in order to assess the risk of stroke. In our work, we provide reliable confidence measures for the assessment of stroke ...

    • Conference Object  

      Comparative analysis of artificial neural network models: Application in bankruptcy prediction 

      Charalambous, Chris; Charitou, Andreas; Kaourou, Froso (IEEE, 1999)
      This study compares the predictive performance of three neural network methods, namely the Learning Vector Quantization, Radial Basis Function, the Feedforward network that uses the conjugate gradient optimization algorithm, ...

    • Conference Object  

      Deriving quantitative structure-activity relationship models using genetic programming for drug discovery 

      Neophytou, K.; Nicolaou, Christos A.; Pattichis, Constantinos S.; Schizas, Christos N. (2008)
      Genetic Programming is a heuristic search algorithm inspired by evolutionary techniques that has been shown to produce satisfactory solutions to problems related to several scientific domains [1]. Presented here is a ...

    • Article  

      Evaluation of the risk of stroke with confidence predictions based on ultrasound carotid image analysis 

      Lambrou, Alexandra; Papadopoulos, Harris; Kyriacou, Efthyvoulos C.; Pattichis, Constantinos S.; Pattichis, Marios S.; Gammerman, A.; Nicolaïdes, Andrew N. (2012)
      Conformal Predictors (CPs) are Machine Learning algorithms that can provide reliable confidence measures to their predictions. In this work, we make use of the Conformal Prediction framework for the assessment of stroke ...

    • Article  

      An extension of a hierarchical reinforcement learning algorithm for multiagent settings 

      Lambrou, Ioannis; Vassiliades, Vassilis; Christodoulou, Chris C. (2012)
      This paper compares and investigates single-agent reinforcement learning (RL) algorithms on the simple and an extended taxi problem domain, and multiagent RL algorithms on a multiagent extension of the simple taxi problem ...

    • Article  

      Learning non-monotonic logic programs: Learning exceptions 

      Dimopoulos, Yannis; Kakas, Antonis C. (1995)
      In this paper we present a framework for learning non-monotonic logic programs. The method is parametric on a classical learning algorithm whose generated rules are to be understood as default rules. This means that these ...

    • Conference Object  

      Multi feature texture analysis for the classification of carotid plaques 

      Christodoulou, Christodoulos I.; Pattichis, Constantinos S.; Pantzaris, Marios C.; Tegos, Thomas J.; Nicolaïdes, Andrew N.; Elatrozy, Tarek S.; Sabetai, Michael; Dhanjil, S. (IEEE, 1999)
      The objective of this work was to develop a computer aided system which will facilitate the automated characterization of carotid plaques recorded from high resolution ultrasound images for the identification of individuals ...

    • Article  

      Multiagent reinforcement learning: Spiking and nonspiking agents in the Iterated Prisoner's Dilemma 

      Vassiliades, Vassilis; Cleanthous, A.; Christodoulou, Chris C. (2011)
      This paper investigates multiagent reinforcement learning (MARL) in a general-sum game where the payoffs' structure is such that the agents are required to exploit each other in a way that benefits all agents. The contradictory ...

    • Conference Object  

      Near optimal wireless data broadcasting based on an unsupervised neural network learning algorithm 

      Vlajic, N.; Makrakis, D.; Charalambous, Charalambos D. (2001)
      Wireless Data Broadcasting (WDB) is proven to be an efficient information delivery mechanism of nearly unlimited scalability. However, successful performance of a WDB based system is not always guaranteed - it strongly ...

    • Conference Object  

      New technique for the classification and decomposition of EMG signals 

      Christodoulou, Christodoulos I.; Pattichis, Constantinos S. (IEEE, 1995)
      The shapes and firing rates of motor unit action potentials (MUAPs) in an electromyographic (EMG) signal provide an important source of information for the diagnosis of neuromuscular disorders. In order to extract this ...

    • Article  

      Protein secondary structure prediction with bidirectional recurrent neural nets: Can weight updating for each residue enhance performance? 

      Agathocleous, Michalis; Christodoulou, Georgia; Promponas, Vasilis J.; Christodoulou, Chris C.; Vassiliades, Vassilis; Antoniou, Antonis (2010)
      Successful protein secondary structure prediction is an important step towards modelling protein 3D structure, with several practical applications. Even though in the last four decades several PSSP algorithms have been ...

    • Conference Object  

      Scalable and dynamic global power management for multicore chips 

      Otoom, M.; Trancoso, Pedro; Almasaeid, H.; Alzubaidi, M. (Association for Computing Machinery, 2015)
      The design for continuous computer performance is increasingly becoming limited by the exponential increase in the power consumption. In order to improve the energy efficiency of multicore chips, we propose a novel global ...

    • Article  

      Toward automated generation of parametric BIMs based on hybrid video and laser scanning data 

      Brilakis, Ioannis; Lourakis, Manolis; Sacks, Rafael; Savarese, Silvio; Christodoulou, Symeon E.; Teizer, Jochen; Makhmalbaf, Atefe (2010)
      Only very few constructed facilities today have a complete record of as-built information. Despite the growing use of Building Information Modelling and the improvement in as-built records, several more years will be ...

    • Article  

      Training bidirectional recurrent neural network architectures with the scaled conjugate gradient algorithm 

      Agathocleous, Michalis; Christodoulou, Chris C.; Promponas, Vasilis J.; Kountouris, P.; Vassiliades, Vassilis (2016)
      Predictions on sequential data, when both the upstream and downstream information is important, is a difficult and challenging task. The Bidirectional Recurrent Neural Network (BRNN) architecture has been designed to deal ...

    • Conference Object  

      Variable target values neural network for dealing with extremely imbalanced datasets 

      Karatsiolis, Savvas; Schizas, Christos N. (Springer Verlag, 2016)
      An original classification algorithm is proposed for dealing with extremely imbalanced datasets that often appear in biomedical problems. Its originality comes from the way a neural network is trained in order to get a ...

    • Article  

      Web robot detection: A probabilistic reasoning approach 

      Stassopoulou, Athena; Dikaiakos, Marios D. (2009)
      In this paper, we introduce a probabilistic modeling approach for addressing the problem of Web robot detection from Web-server access logs. More specifically, we construct a Bayesian network that classifies automatically ...

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