Recent Submissions

  • On the use of CBR in optimisation problems such as the TSP 

    Cunningham, Padraig; Smyth, Barry; Hurley, Neil (Trinity College Dublin, Department of Computer Science, 1995-06)
    The particular strength of CBR is normally considered to be its use in weak theory domains where solution quality is compiled into cases and is reusable. In this paper we explore an alternative use of CBR in optimisati ...
  • Wireless Communication Using Real-Time Extensions to the Linux Network Subsystem 

    CAHILL, VINNY (2006)
    Timely wireless communication is essential to allow real-time mobile applications, e.g., communication between mobile robots and intervehicle communication to be realized. The current IEEE 802.11 ad hoc protocol is ...
  • Real-Time Communication in IEEE 802.11 Mobile Ad hoc Networks A Feasibility Study 

    CAHILL, VINNY; WEBER, STEFAN; GLEESON, MARK (2006)
    Achieving predictable communication latency in an ad hoc IEEE 802.11 wireless local area network necessitates an approach that overcomes the impact of the underlying non-deterministic contention-based medium access ...
  • Practical Solutions to the Problem of Diagonal Dominance in Kernel Document Clustering 

    Greene, Derek; Cunningham, Padraig (Trinity College Dublin, Department of Computer Science, 2006-02-07)
    In supervised kernel methods, it has been observed that the performance of the SVM classifier is poor in cases where the diagonal entries of the Gram matrix are large relative to the off-diagonal entries. This problem, ...
  • ECUE: A Spam Filter that Uses Machine Learning to Track Concept Drift 

    Delany, Sarah Jane; Cunningham, Padraig (Trinity College Dublin, Department of Computer Science, 2006-02-10)
    While text classification has been identified for some time as a promising application area for Artificial Intelligence, so far few deployed applications have been described. In this paper we present a spam filtering ...
  • Predicting Probability Distributions for Surf Height Using an Ensemble of Mixture Density Networks 

    Carney, Michael; Cunningham, Padraig; Dowling, Jim (Trinity College Dublin, Department of Computer Science, 2006-02-10)
    There is a range of potential applications of Machine Learning where it would be more useful to predict the probability distribution for a variable rather than simply the most likely value for that variable. In meteorology ...
  • Calibrating Probability Density Forecasts with Multi-objective Search 

    Carney, Michael; Cunningham, Padraig (Trinity College Dublin, Department of Computer Science, 2006-02-10)
    In this paper, we show that the optimization of density forecasting models for regression in machine learning can be formulated as a multi-objective problem.We describe the two objectives of sharpness and calibration and ...
  • An Evaluation of the Usefulness of Explanation in a CBR System for Decision Support in Bronchiolitis Treatment 

    Doyle, Donal; Cunningham, Padraig (Trinity College Dublin, Department of Computer Science, 2006-04-04)
    The research presented here explores the hypothesis that the deployment and acceptance of decision support systems in medicine will be enhanced if the basis for the recommendation produced by the system is apparent. We ...
  • Evaluating Density Forecasting Models 

    Carney, Michael; Cunningham, Padraig (Trinity College Dublin, Department of Computer Science, 2006-05-02)
    Density forecasting in regression is gaining popularity as real world applications demand an estimate of the level of uncertainty in predictions. In this paper we describe the two goals of density forecasting1 sharpness ...
  • Efficient Prediction-Based Validation for Document Clustering 

    Greene, Derek; Cunningham, Padraig (Trinity College Dublin, Department of Computer Science, 2006-05-02)
    Recently, stability-based techniques have emerged as a very promising solution to the problem of cluster validation. An inherent drawback of these approaches is the computational cost of generating and assessing multiple ...
  • Dynamic Integration with Random Forests 

    Tsymbal, Alexey; Cunningham, Padraig (Trinity College Dublin, Department of Computer Science, 2006)
    Random Forests are a successful ensemble prediction technique that combines two sources of randomness to generate base decision trees; bootstrapping instances for each tree and considering a random subset of features ...
  • Does Relevance Matter to Data Mining Research? 

    Tsymbal, Alexey (Trinity College Dublin, Department of Computer Science, 2006)
    Data mining (DM) and knowledge discovery are intelligent tools that help to accumulate and process data and make use of it. We review several existing frameworks for DM research that originate from different paradigms. ...
  • Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction 

    Tsymbal, Alexey (Trinity College Dublin, Department of Computer Science, 2006)
    Inductive learning systems have been successfully applied in a number of medical domains. It is generally accepted that the highest accuracy results that an inductive learning system can achieve depend on the quality ...
  • FacetS: First Class Entities for an Open Dynamic AOP Language 

    Bergel, Alexandre (Trinity College Dublin, Department of Computer Science, 2006)
    This paper describes a new aspect language construct for Squeak, named FACETS. Aspects are completely integrated within the Squeak programming language and its environment. The innovations of FACETS are: (i) traits can ...
  • Feature Extraction for Dynamic Integration of Classifiers 

    Tsymbal, Alexey (Trinity College Dublin, Department of Computer Science, 2006)
    Recent research has shown the integration of multiple classifiers to be one of the most important directions in machine learning and data mining. In this paper, we present an algorithm for the dynamic integration of ...
  • Context-Aware Aspects 

    Bergel, Alexandre (Trinity College Dublin, Department of Computer Science, 2006)
    Context-aware applications behave differently depending on the context in which they are running. Since context-specific behaviour tends to crosscut base programs, it can advantageously be implemented as aspects. This ...
  • Adaptive Offset Subspace Self-Organizing Map: An Application to Handwritten Digit Recognition 

    Zheng, Huicheng; Cunningham, Padraig; Tsymbal, Alexey (Trinity College Dublin, Department of Computer Science, 2006-06-23)
    An Adaptive-Subspace Self-Organizing Map (ASSOM) can learn a set of ordered linear subspaces which correspond to invariant classes. However the basic ASSOMcannot properly learn linear manifolds that are shifted away ...
  • Object Recognition and Active Learning in Microscope Images 

    Nugent, Conor; Cunningham, Padraig (Trinity College Dublin, Department of Computer Science, 2006-07-26)
    Microscopic analysis forms an integral part of many scientific studies. It is a task which requires great expertise and care. However, it can often be an extremely repetitive and laborious task. In some cases many ...
  • Overfitting in Wrapper-Based Feature Subset Selection: The Harder You Try the Worse it Gets 

    Cunningham, Padraig; Loughrey, John (Trinity College Dublin, Department of Computer Science, 2005-01-28)
    In Wrapper based feature selection, the more states that are visited during the search phase of the algorithm the greater the likelihood of finding a feature subset that has a high internal accuracy while generalizing ...
  • A Comparison of Ensemble and Case-Base Maintenance Techniques for Handling Concept Drift in Spam Filtering 

    Delany, Sarah Jane; Cunningham, Padraig; Tsymbal, Alexey (Trinity College Dublin, Department of Computer Science, 2005)
    The problem of concept drift has recently received considerable attention in machine learning research. One important practical problem where concept drift needs to be addressed is spam filtering. The literature on ...

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