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'trinity' in all fields;
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Displaying Results 76 - 100 of 6252 on page 4 of 251
Marked
Mark
Flesh wounds?: new ways of understanding self-injury
(2008)
Inckle, Kay
Flesh wounds?: new ways of understanding self-injury
(2008)
Inckle, Kay
Abstract:
My research uses methodological and representational practices from the humanities and arts in order to develop a non-medical understanding of self-injury. The aims of the project are: to use creative practices to promote an accessible person-centred understanding of self-injury, from a holistic, harm-reduction, embodied perspective; to increase points of dialogue between all perspectives involved in and/or affected by self-injury; to illustrate both positive and negative responses to, and interventions in, self-injury and to highlight their impacts for the individuals concerned.
http://hdl.handle.net/2262/20843
Marked
Mark
Towards a research platform for the 1641 Depositions: new technologies and the humanities
(2008)
LAWLESS, SÉAMUS; WADE, VINCENT PATRICK; Ó SIOCHRÚ, MICHÉAL
Towards a research platform for the 1641 Depositions: new technologies and the humanities
(2008)
LAWLESS, SÉAMUS; WADE, VINCENT PATRICK; Ó SIOCHRÚ, MICHÉAL
Abstract:
This project will investigate innovative technology to structure and lend meaning to a large corpus of unstructured information. The objectives of this project are: to investigate how text analytics and semantic mark-up tools can be used to enable researchers to discover new insights contained within the digital humanities; to capture some aspects of the subtle processes by which researchers model the meaning of documents and to make key aspects of their knowledge available to other researchers; to prototype and evaluate selected tools which achieve these objectives, using the transcribed 1641 Depositions as a corpus of unstructured humanities content.
http://hdl.handle.net/2262/20842
Marked
Mark
Irresistible toy or social leveller: motion pictures in Irish life, 1900 - 1939
(2008)
Downey, Ann
Irresistible toy or social leveller: motion pictures in Irish life, 1900 - 1939
(2008)
Downey, Ann
Abstract:
This research examines the cinema as an important alternative public sphere in 1920s and 1930s Ireland. The cinema-going public in Ireland during the first two decades of the Free State's existence kept in touch with the modern world in a way that balanced and modified the prevailing influence of the nationalist inward-looking world-view of Ireland at that time. Cinema was a notable agent of change during that crucial time as it was the main interface for the masses of Irish people with modernism and the greater outside/Western world at that time.
http://hdl.handle.net/2262/20844
Marked
Mark
Second language acquisition and native language maintenance in the Polish diaspora in Ireland and France
(2008)
Kopeckova, Romana; Singleton, David; Regan, Vera; Debaene, Ewelina
Second language acquisition and native language maintenance in the Polish diaspora in Ireland and France
(2008)
Kopeckova, Romana; Singleton, David; Regan, Vera; Debaene, Ewelina
Abstract:
Since the accession of ten new member states to the EU on May 1st 2004, the Republic of Ireland has experienced a significant reversal in the direction of migration - from outward to inward. Large numbers of Polish nationals have arrived here in search of better life and better career prospects. Our project seeks to investigate this community, the dominant migrant community living in Ireland, with respect to their acquisition and use of the English language as well as their maintenance of Polish. The project promises to yield findings of a sociolinguistic, psycholinguistic, sociocultural and educational nature.
http://hdl.handle.net/2262/20841
Marked
Mark
Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction
(2006)
Tsymbal, Alexey
Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction
(2006)
Tsymbal, Alexey
Abstract:
TCD-CS-2006-25
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 of data and on the appropriate selection of a learning algorithm for the data. In this paper we analyze the effect of class noise on supervised learning in medical domains. We review the related work on learning from noisy data and propose to use feature extraction as a pre-processing step to diminish the effect of class noise on the learning process. Our experiments with 8 medical datasets show that feature extraction indeed helps to deal with class noise. It clearly results in higher classification accuracy of learnt models without the separate explicit elimination of noisy instances.
http://hdl.handle.net/2262/13498
Marked
Mark
Does Relevance Matter to Data Mining Research?
(2006)
Tsymbal, Alexey
Does Relevance Matter to Data Mining Research?
(2006)
Tsymbal, Alexey
Abstract:
TCD-CS-2006-24
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. These DM frameworks mainly address various DM algorithms for the different steps of the DM process. Recent research has shown that many real-world problems require integration of several DM algorithms from different paradigms in order to produce a better solution elevating the importance of practice-oriented aspects also in DM research. In this paper we strongly emphasize that DM research should also take into account the relevance of research, not only the rigor of it. Under relevance of research in general, we understand how good this research is in terms of the utility of its results. This chapter motivates development of such a new framework for DM research that would explicitly include the concept of relevance. We introduce the basic idea behin...
http://hdl.handle.net/2262/13499
Marked
Mark
Dynamic Integration with Random Forests
(2006)
Tsymbal, Alexey; Cunningham, Pádraig
Dynamic Integration with Random Forests
(2006)
Tsymbal, Alexey; Cunningham, Pádraig
Abstract:
TCD-CS-2006-23
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 at each node. Breiman in his introductory paper on Random Forests claims that they are more robust than boosting with respect to overfitting noise, and are able to compete with boosting in terms of predictive performance. Multiple recently published empirical studies conducted in various application domains confirm these claims. Random Forests use simple majority voting to combine the predictions of the trees. However, it is clear that each decision tree in a random forest may have different contribution in classifying a certain instance. In this paper, we demonstrate that the prediction performance of Random Forests may still be improved in some domains by replacing the combination function. Dynamic integration, which is based on local performance es...
http://hdl.handle.net/2262/13500
Marked
Mark
Efficient Prediction-Based Validation for Document Clustering
(2006)
Greene, Derek; Cunningham, Pádraig
Efficient Prediction-Based Validation for Document Clustering
(2006)
Greene, Derek; Cunningham, Pádraig
Abstract:
TCD-CS-2006-22
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 clusterings of the data. In this paper we present an efficient prediction-based validation approach suitable for application to large, high-dimensional datasets such as text corpora. We use kernel clustering to isolate the validation procedure from the original data. Furthermore, we employ a prototype reduction strategy that allows us to work on a reduced kernel matrix, leading to significant computational savings. To ensure that this condensed representation accurately reflects the cluster structures in the data, we propose a density-biased selection strategy. This novel validation process is evaluated on a large number of real and artificial datasets, where it is shown to consistently produce good estimates for the optimal number of clusters.
http://hdl.handle.net/2262/13501
Marked
Mark
Evaluating Density Forecasting Models
(2006)
Carney, Michael; Cunningham, Pádraig
Evaluating Density Forecasting Models
(2006)
Carney, Michael; Cunningham, Pádraig
Abstract:
TCD-CS-2006-21
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 and calibration. We review the evaluation methods available to a density forecaster to assess each of these goals and we introduce a new evaluation method that allows modelers to compare and evaluate their models across both of these goals simultaneously and identify the optimal model.
http://hdl.handle.net/2262/13502
Marked
Mark
An Evaluation of the Usefulness of Explanation in a CBR System for Decision Support in Bronchiolitis Treatment
(2006)
Doyle, Dónal; Cunningham, Pádraig
An Evaluation of the Usefulness of Explanation in a CBR System for Decision Support in Bronchiolitis Treatment
(2006)
Doyle, Dónal; Cunningham, Pádraig
Abstract:
TCD-CS-2006-17
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 describe a decision support system for advising on patients suffering from bronchiolitis. This system supports its recommendations with precedent cases selected to support the recommendation along with justification text that highlights aspects of these cases relevant to the query case. It also presents an estimate of its confidence in the recommendation. The main contribution of this paper is an evaluation of this system in a clinical context. The evaluation shows that this type of explanation does enhance the usefulness of the system for practitioners.
http://hdl.handle.net/2262/13503
Marked
Mark
Calibrating Probability Density Forecasts with Multi-objective Search
(2006)
Carney, Michael; Cunningham, Pádraig
Calibrating Probability Density Forecasts with Multi-objective Search
(2006)
Carney, Michael; Cunningham, Pádraig
Abstract:
TCD-CS-2006-07
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 suggest suitable scoring metrics for both.We use the popular negative log-likelihood as a measure of sharpness and the probability integral transform as a measure of calibration. We show how optimization on negative log-likelihood alone often results in sub-optimal models. To solve this problem we introduce a multi-objective evolutionary optimization framework that can produce better density forecasts from a prediction users perspective. Our experiments show improvements over state-of-the-art approaches.
http://hdl.handle.net/2262/13504
Marked
Mark
Predicting Probability Distributions for Surf Height Using an Ensemble of Mixture Density Networks
(2006)
Carney, Michael; Cunningham, Pádraig; Dowling, Jim
Predicting Probability Distributions for Surf Height Using an Ensemble of Mixture Density Networks
(2006)
Carney, Michael; Cunningham, Pádraig; Dowling, Jim
Abstract:
TCD-CS-2006-06
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 and in finance it is often important to know the probability of a variable falling within (or outside) different ranges. In this paper we consider the prediction of surf height with the objective of predicting if it will fall within a given ‘surfable’ range. Prediction problems such as this are considerably more difficult if the distribution of the phenomenon is significantly different from a normal distribution. This is the case with the surf data we have studied. To address this we use an ensemble of mixture density networks to predict the probability density function. Our evaluation shows that this is an effective solution. We also describe a web-based application that presents these predictions in a usable manner.
http://hdl.handle.net/2262/13505
Marked
Mark
ECUE: A Spam Filter that Uses Machine Learning to Track Concept Drift
(2006)
Delany, Sarah Jane; Cunningham, Pádraig
ECUE: A Spam Filter that Uses Machine Learning to Track Concept Drift
(2006)
Delany, Sarah Jane; Cunningham, Pádraig
Abstract:
TCD-CS-2006-05
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 system that uses example-based machine learning techniques to train a classifier from examples of spam and legitimate email. This approach has the advantage that it can personalise to the specifics of the user’s filtering preferences. This classifier can also automatically adjust over time to account for the changing nature of spam (and indeed changes in the profile of legitimate email). A significant software engineering challenge in developing this system was to ensure that it could interoperate with existing email systems to allow easy managment of the training data over time. This system has been deployed and evaluated over an extended period and the results of this evaluation are presented here.
http://hdl.handle.net/2262/13506
Marked
Mark
Practical Solutions to the Problem of Diagonal Dominance in Kernel Document Clustering
(2006)
Greene, Derek; Cunningham, Pádraig
Practical Solutions to the Problem of Diagonal Dominance in Kernel Document Clustering
(2006)
Greene, Derek; Cunningham, Pádraig
Abstract:
TCD-CS-2006-04
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, referred to as diagonal dominance, often occurs when certain kernel functions are applied to sparse high-dimensional data, such as text corpora. In this paper we investigate the implications of diagonal dominance for unsupervised kernel methods, specifically in the task of document clustering. We discuss a selection of strategies for addressing this issue, and evaluate their effectiveness in producing more accurate and stable clusterings.
http://hdl.handle.net/2262/13518
Marked
Mark
FacetS: First Class Entities for an Open Dynamic AOP Language
(2006)
Bergel, Alexandre
FacetS: First Class Entities for an Open Dynamic AOP Language
(2006)
Bergel, Alexandre
Abstract:
TCD-CS-2006-31
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 be part of the pointcut definition, (ii) two scoping policies are available to share state among aspects and (iii) aspects are prototype-based.
http://hdl.handle.net/2262/13478
Marked
Mark
Feature Extraction for Dynamic Integration of Classifiers
(2006)
Tsymbal, Alexey
Feature Extraction for Dynamic Integration of Classifiers
(2006)
Tsymbal, Alexey
Abstract:
TCD-CS-2006-32
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 classifiers in the space of extracted features (FEDIC). It is based on the technique of dynamic integration, in which local accuracy estimates are calculated for each base classifier of an ensemble, in the neighborhood of a new instance to be processed. Generally, the whole space of original features is used to find the neighborhood of a new instance for local accuracy estimates in dynamic integration. However, when dynamic integration takes place in high dimensions the search for the neighborhood of a new instance is problematic, since the majority of space is empty and neighbors can in fact be located far from each other. Furthermore, when noisy or irrelevant features are present it is likely that also irrelevant neighbors will be associated with a t...
http://hdl.handle.net/2262/13461
Marked
Mark
Context-Aware Aspects
(2006)
Bergel, Alexandre
Context-Aware Aspects
(2006)
Bergel, Alexandre
Abstract:
TCD-CS-2006-35
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 leads to the notion of context-aware aspects, i.e., aspects whose behaviour depends on context. This paper analyzes the issue of appropriate support from the aspect language to both restrict the scope of aspects according to the context and allow aspect definitions to access information associated to the context.We propose an open framework for context-aware aspects that allows for the definition of first-class contexts and supports the definition of context awareness constructs for aspects, including the ability to refer to past contexts, and to provide domainand application-specific constructs.
http://hdl.handle.net/2262/13460
Marked
Mark
Adaptive Offset Subspace Self-Organizing Map: An Application to Handwritten Digit Recognition
(2006)
Zheng, Huicheng; Cunningham, Pádraig; Tsymbal, Alexey
Adaptive Offset Subspace Self-Organizing Map: An Application to Handwritten Digit Recognition
(2006)
Zheng, Huicheng; Cunningham, Pádraig; Tsymbal, Alexey
Abstract:
TCD-CS-2006-36
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 from the origin of the input space. In this paper, we propose an improvement on ASSOM to amend this deficiency. The new network, named AOSSOM for Adaptive Offset Subspace Self-Organizing Map, minimizes a projection error function in a gradient-descent fashion. In each learning step, the winning module and its neighbors update their offset vectors and basis vectors of the target manifolds towards the negative gradient of the error function. We show by experiments that the AOSSOM can learn clusters aligned on linear manifolds shifted away from the origin and separate them accordingly. The proposed AOSSOM is applied to handwritten digit recognition and shows promising results.
http://hdl.handle.net/2262/13459
Marked
Mark
Object Recognition and Active Learning in Microscope Images
(2006)
Nugent, Conor; Cunningham, Pádraig
Object Recognition and Active Learning in Microscope Images
(2006)
Nugent, Conor; Cunningham, Pádraig
Abstract:
TCD-CS-2006-45
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 hundreds of slides may need to analysed, a process that will require each slide to be meticulously examined. Machine vision tools could be used to help assist in just such repetitive and tedious tasks. However, many machine vision solutions involve a lengthy data acquisition phase and in many cases result in systems that are highly specialised and not easily adaptable. In this paper, we describe a framework that applies flexible machine vision techniques to microscope analysis and utilises active learning to help overcome the data acquisition and adaptability problems. In particular we investigate the potential of various aspects of our proposed framework on a particular real world microscopic task, the recognition of parasite eggs.
http://hdl.handle.net/2262/13458
Marked
Mark
Overfitting in Wrapper-Based Feature Subset Selection: The Harder You Try the Worse it Gets
(2005)
Cunningham, Pádraig; Loughrey, John
Overfitting in Wrapper-Based Feature Subset Selection: The Harder You Try the Worse it Gets
(2005)
Cunningham, Pádraig; Loughrey, John
Abstract:
TCD-CS-2005-17
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 poorly. When this occurs, we say that the algorithm has overfitted to the training data. We outline a set of experiments to show this and we introduce a modified genetic algorithm to address this overfitting problem by stopping the search before overfitting occurs. This new algorithm called GAWES (Genetic Algorithm With Early Stopping) reduces the level of overfitting and yields feature subsets that have a better generalization accuracy.
http://hdl.handle.net/2262/13440
Marked
Mark
Sequential Genetic Search for Ensemble Feature Selection
(2005)
Tsymbal, Alexey; Cunningham, Pádraig
Sequential Genetic Search for Ensemble Feature Selection
(2005)
Tsymbal, Alexey; Cunningham, Pádraig
Abstract:
TCD-CS-2005-40
Ensemble learning constitutes one of the main directions in machine learning and data mining. Ensembles allow us to achieve higher accuracy, which is often not achievable with single models. One technique, which proved to be effective for constructing an ensemble of diverse classifiers, is the use of feature subsets. Among different approaches to ensemble feature selection, genetic search was shown to perform best in many domains. In this paper, a new strategy GAS-SEFS, Genetic Algorithm-based Sequential Search for Ensemble Feature Selection, is introduced. Instead of one genetic process, it employs a series of processes, the goal of each of which is to build one base classifier. Experiments on 21 data sets are conducted, comparing the new strategy with a previously considered genetic strategy for different ensemble sizes and for five different ensemble integration methods. The experiments show that GAS-SEFS, although being more time-consuming, often builds better...
http://hdl.handle.net/2262/13358
Marked
Mark
Using Early-Stopping to Avoid Overfitting in Wrapper-Based Feature Selection Employing Stochastic Search
(2005)
Loughrey, John; Cunningham, Pádraig
Using Early-Stopping to Avoid Overfitting in Wrapper-Based Feature Selection Employing Stochastic Search
(2005)
Loughrey, John; Cunningham, Pádraig
Abstract:
TCD-CS-2005-37
It is acknowledged that overfitting can occur in feature selection using the wrapper method when there is a limited amount of training data available. It has also been shown that the severity of overfitting is related to the intensity of the search algorithm used during this process. In this paper we show that two stochastic search techniques (Simulated Annealing and Genetic Algorithms) that can be used for wrapper-based feature selection are susceptible to overfitting in this way. However, because of their stochastic nature, these algorithms can be stopped early to prevent overfitting. We present a framework that implements early-stopping for both of these stochastic search techniques and we show that this is successful in reducing the effects of overfitting and in increasing generalisation accuracy in most cases.
http://hdl.handle.net/2262/13359
Marked
Mark
On the use of Information Systems Research Methods in Data Mining
(2005)
Tsymbal, Alexey
On the use of Information Systems Research Methods in Data Mining
(2005)
Tsymbal, Alexey
Abstract:
TCD-CS-2005-31
http://hdl.handle.net/2262/13360
Marked
Mark
Meta-Knowledge Management in MultiStrategy Process-Oriented Knowledge Discovery Systems
(2005)
Tsymbal, Alexey
Meta-Knowledge Management in MultiStrategy Process-Oriented Knowledge Discovery Systems
(2005)
Tsymbal, Alexey
Abstract:
TCD-CS-2005-30
http://hdl.handle.net/2262/13361
Marked
Mark
Blame-Based Noise Reduction: An Alternative Perspective on Noise Reduction for Lazy Learning
(2005)
Pasquier, François-Xavier; Delany, Sarah Jane; Cunningham, Pádraig
Blame-Based Noise Reduction: An Alternative Perspective on Noise Reduction for Lazy Learning
(2005)
Pasquier, François-Xavier; Delany, Sarah Jane; Cunningham, Pádraig
Abstract:
TCD-CS-2005-29
In this paper we present a new perspective on noise reduction for nearest-neighbour classifiers. Classic noise reduction algorithms such as Repeated Edited Nearest Neighbour remove cases from the training set if they are misclassified by their nearest neighbours in a leave-one-out cross validation. In the approach presented here, cases are identified for deletion based on their propensity to cause misclassifications. This approach was originally identified in a case-based spam filtering application where it became clear that certain training examples were damaging to the accuracy of the system. In this paper we evaluate the general applicability of the approach on a large variety of datasets and show that it generally beats the classic approach. We also compare the two techniques on artificial noise and show that both are far from perfect at removing noise and that there remains scope for further research in this area.
http://hdl.handle.net/2262/13378
Displaying Results 76 - 100 of 6252 on page 4 of 251
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