Institutions
|
About Us
|
Help
|
Gaeilge
0
1000
Home
Browse
Advanced Search
Search History
Marked List
Statistics
A
A
A
Show search options
Hide search options
Search using:
All
Any
None of these
Exact Phrase
in
Keyword (All Fields)
Title
Author
Subject
Institution
Funder
All
Any
None of these
Exact Phrase
in
Keyword (All Fields)
Title
Author
Subject
Institution
Funder
All
Any
None of these
Exact Phrase
in
Keyword (All Fields)
Title
Author
Subject
Institution
Funder
From
2104
2027
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979
1978
1977
1976
1975
1974
1973
1972
1971
1970
1969
1968
1967
1966
1965
1964
1963
1962
1961
1960
1959
1958
1957
1956
1955
1954
1953
1952
1951
1950
1949
1948
1947
1946
1945
1944
1943
1942
1941
1940
1939
1938
1937
1936
1935
1934
1933
1932
1931
1930
1929
1928
1927
1926
1925
1924
1923
1922
1921
1920
1919
1918
1917
1916
1915
1914
1913
1912
1911
1910
1909
1908
1907
1906
1905
1904
1903
1902
1901
1900
1899
1898
1897
1896
1895
1894
1893
1892
1891
1890
1889
1888
1887
1886
1885
1884
1883
1882
1881
1880
1879
1878
1877
1876
1875
1874
1873
1872
1871
1870
1869
1868
1867
1866
1865
1864
1863
1862
1861
1860
1859
1858
1857
1856
1855
1854
1853
1852
1851
1850
1849
1846
1842
1840
1839
1835
1827
1825
1821
1820
1818
1817
1815
1812
1811
1810
1809
1808
1807
1806
1805
1804
1803
1802
1801
1800
1792
1790
1770
1713
1111
1000
To
2104
2027
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979
1978
1977
1976
1975
1974
1973
1972
1971
1970
1969
1968
1967
1966
1965
1964
1963
1962
1961
1960
1959
1958
1957
1956
1955
1954
1953
1952
1951
1950
1949
1948
1947
1946
1945
1944
1943
1942
1941
1940
1939
1938
1937
1936
1935
1934
1933
1932
1931
1930
1929
1928
1927
1926
1925
1924
1923
1922
1921
1920
1919
1918
1917
1916
1915
1914
1913
1912
1911
1910
1909
1908
1907
1906
1905
1904
1903
1902
1901
1900
1899
1898
1897
1896
1895
1894
1893
1892
1891
1890
1889
1888
1887
1886
1885
1884
1883
1882
1881
1880
1879
1878
1877
1876
1875
1874
1873
1872
1871
1870
1869
1868
1867
1866
1865
1864
1863
1862
1861
1860
1859
1858
1857
1856
1855
1854
1853
1852
1851
1850
1849
1846
1842
1840
1839
1835
1827
1825
1821
1820
1818
1817
1815
1812
1811
1810
1809
1808
1807
1806
1805
1804
1803
1802
1801
1800
1792
1790
1770
1713
1111
1000
Optionally, filter by:
(Leave unchecked to search all fields)
Item Type
Book
Book chapter
Conference item
Contribution to newspaper/magazine
Doctoral thesis
Journal article
Master thesis (research)
Master thesis (taught)
Multimedia
Patent
Report
Review
Working paper
Other
Peer Review Status
Peer reviewed
Non peer reviewed
Unknown
Institution
All Ireland Public Health Repository
Connacht-Ulster Alliance
Dublin City University
Dublin Institute of Technology
Dundalk Institute of Technology
Lenus
Marine Institute
Mary Immaculate College
Maynooth University
NUI Galway
Royal College of Surgeons in Ireland
Teagasc
Trinity College Dublin
University College Cork
University College Dublin
University of Limerick
Funder
Enterprise Ireland (EI)
Environmental Protection Agency (EPA)
Health Research Board (HRB)
Higher Education Authority (HEA)
Irish Aid
Irish Research Council for Humanities and Social Sciences (IRCHSS)
Irish Research Council for Science Engineering and Technology (IRCSET)
Marine Institute
Science Foundation Ireland (SFI)
Teagasc
Language
Irish
English
Bulgarian
Catalan; Valencian
Chinese
Czech
Danish
Dutch; Flemish
Estonian
French
German
Greek, Modern (1453-)
Croatian
Interlingue; Occidental
Italian
Japanese
Korean
Lithuanian
Norwegian
Polish
Portuguese
Romanian; Moldavian; Moldovan
Spanish; Castilian
Serbian
Turkish
Vietnamese
Current Search:
All of 'Computer' and 'Science' in all fields;
3088 items found
Sort by
Relevance
Title
Author
Item type
Date
Institution
Peer review status
Language
Order
Ascending
Descending
25
50
100
per page
1
2
3
4
5
6
7
8
9
10
11
Bibtex
CSV
EndNote
RefWorks
RIS
XML
Displaying Results 126 - 150 of 3088 on page 6 of 124
Marked
Mark
Object Recognition and Active Learning in Microscope Images
(2006)
Nugent, Conor; Cunningham, Padraig
Object Recognition and Active Learning in Microscope Images
(2006)
Nugent, Conor; Cunningham, Padraig
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
Generating Estimates of Classification Confidence for a Case-Based Spam Filter
(2005)
Delany, Sarah Jane; Cunningham, Padraig; Doyle, Donal
Generating Estimates of Classification Confidence for a Case-Based Spam Filter
(2005)
Delany, Sarah Jane; Cunningham, Padraig; Doyle, Donal
Abstract:
TCD-CS-2005-20
Producing estimates of classification confidence is surprisingly difficult. One might expect that classifiers that can produce numeric classification scores (e.g. k-Nearest Neighbour or Naive Bayes) could readily produce confidence estimates based on thresholds. In fact, this proves not to be the case, probably because these are not probabilistic classifiers in the strict sense. The numeric scores coming from k-Nearest Neighbour or Naive Bayes classifiers are not well correlated with classification confidence. In this paper we describe a case-based spam filtering application that would benefit significantly from an ability to attach confidence predictions to positive classifications (i.e. messages classified as spam). We show that `obvious? confidence metrics for a case-based classifier are not effective. We propose an ensemble-like solution that aggregates a collection of confidence metrics and show that this offers an effective solution in this spam filtering do...
http://hdl.handle.net/2262/13438
Marked
Mark
The Best Way to Instil Confidence is by Being Right An Evaluation of the Effectiveness of Case-Based Explanations in providing User Confidence
(2005)
Nugent, Conor; Cunningham, Padraig; Doyle, Donal
The Best Way to Instil Confidence is by Being Right An Evaluation of the Effectiveness of Case-Based Explanations in providing User Confidence
(2005)
Nugent, Conor; Cunningham, Padraig; Doyle, Donal
Abstract:
TCD-CS-2005-21
Instilling confidence in the abilities of machine learning systems in end-users is seen as critical to their success in real world problems. One way in which this can be achieved is by providing users with interpretable explanations of the system's predictions. CBR systems have long been understood to have an inherent transparency that has particular advantages for explanations compared with other machine learning techniques. However simply suppling the most similar case is often not enough. In this paper we present a framework for providing interpretable explanations of CBR systems which includes dynamically created discursive texts explaining the feature-value relationships and a measure of confidence of the CBR systems prediction being correct. We also present the results of a preliminary user evaluation we have carried out on the framework.It is clear from this evaluation that being right is important. It appears that caveats and notes of caution when the...
http://hdl.handle.net/2262/13418
Marked
Mark
Dynamic Integration of Classifiers for Tracking Concept Drift in Antibiotic Resistance Data
(2005)
Tsymbal, Alexey; Cunningham, Padraig
Dynamic Integration of Classifiers for Tracking Concept Drift in Antibiotic Resistance Data
(2005)
Tsymbal, Alexey; Cunningham, Padraig
Abstract:
TCD-CS-2005-26
In the real world concepts are often not stable but change with time. A typical example of this in the medical context is antibiotic resistance, where pathogen sensitivity may change over time as new pathogen strains develop resistance to antibiotics which were previously effective. This problem, known as concept drift, complicates the task of learning a model from medical data and requires special approaches, different from commonly used techniques, which treat arriving instances as equally important contributors to the final concept. The underlying data distribution may change as well, making previously built models useless, which is known as virtual concept drift. These changes make regular updates of the model necessary. Among the most popular and effective approaches to handle concept drift is ensemble learning, where a set of models built over different time periods is maintained and the best model is selected or the predictions of models are combined accord...
http://hdl.handle.net/2262/13398
Marked
Mark
Re-using Implicit Knowledge in Short-term Information Profiles for Context-sensitive Tasks
(2005)
Hayes, Conor; Cunningham, Padraig
Re-using Implicit Knowledge in Short-term Information Profiles for Context-sensitive Tasks
(2005)
Hayes, Conor; Cunningham, Padraig
Abstract:
TCD-CS-2005-28
Typically, case-based recommender systems recommend single items to the on-line customer. In this paper we introduce the idea of recommending a user-defined collection of items where the user has implicitly encoded the relationships between the items. Automated collaborative filtering (ACF), a so-called `contentless? technique, has been widely used as a recommendation strategy for music items. However, its reliance on a global model of the user?s interests makes it unsuited to catering for the user?s local interests. We consider the context-sensitive task of building a compilation, a user-defined collection of music tracks. In our analysis, a collection is a case that captures a specific shortterm information/music need. In an offline evaluation, we demonstrate how a case-completion strategy that uses short-term representations is significantly more effective than the ACF technique. We then consider the problem of recommending a compilation according to the user?s...
http://hdl.handle.net/2262/13379
Marked
Mark
Using Early Stopping to Reduce Overfitting in Wrapper-Based Feature Weighting
(2005)
Loughrey, John; Cunningham, Padraig
Using Early Stopping to Reduce Overfitting in Wrapper-Based Feature Weighting
(2005)
Loughrey, John; Cunningham, Padraig
Abstract:
TCD-CS-2005-41
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. We demonstrate that the problem of overfitting in feature weighting can be exacerbated if the feature weighting is fine grained. With greater representational power we risk learning not only the signal, but also the idiosyncrasies of the training data. In this paper we show that both of these effects can be ameliorated by the early-stopping strategy we present. Using this strategy feature weighting will outperform feature selection in most cases.
http://hdl.handle.net/2262/13339
Marked
Mark
The Benefits of Using a Complete Probability Distribution when Decision Making: An Example in Anticoagulant Drug Therapy
(2005)
Carney, Michael; Cunningham, Padraig
The Benefits of Using a Complete Probability Distribution when Decision Making: An Example in Anticoagulant Drug Therapy
(2005)
Carney, Michael; Cunningham, Padraig
Abstract:
TCD-CS-2005-58
In this paper we aim to show how probabilistic prediction of a continuous variable could be more beneficial to a medical practitioner than classification or numeric/point prediction of the same variable in many scenarios. We introduce a probability density forecasting model that produces accurate estimates and achieves statistically consistent predicted distributions. An empirical evaluation of this approach on the problem of warfarin dosage prediction is described and a comparison of results obtained from our probabilistic models with a number of classification techniques on this problem is also shown.
http://hdl.handle.net/2262/13299
Marked
Mark
Representing Similarity for CBR in XML
(2004)
Coyle, Lorcan; Doyle, Donal; Cunningham, Padraig
Representing Similarity for CBR in XML
(2004)
Coyle, Lorcan; Doyle, Donal; Cunningham, Padraig
Abstract:
TCD-CS-2004-25
As Case-Based Reasoning has matured as a discipline; the need for a standard means of representing case-based knowledge has come to the fore. While proposals exist for representing the vocabulary and the case-base knowledge containers, there are still no proposed standards for representing similarity or adaptation knowledge. In this paper we present extensions for representing similarity knowledge to CBML, an XML-based CBR language.
http://hdl.handle.net/2262/13259
Marked
Mark
A Case-Based Technique for Tracking Concept Drift in Spam Filtering
(2004)
Delany, Sarah Jane; Cunningham, Padraig; Tsymbal, Alexey; Coyle, Lorcan
A Case-Based Technique for Tracking Concept Drift in Spam Filtering
(2004)
Delany, Sarah Jane; Cunningham, Padraig; Tsymbal, Alexey; Coyle, Lorcan
Abstract:
TCD-CS-2004-30
Clearly, machine learning techiques can play an important role in filtering spam email because ample training data is available to build a robust classifier. However, spam filtering is a particularly challenging task as the data distribution and concept being learned changeds over time. This is a particularly awkward form of concept drift as the change is driven by spammers wishing to circumvent the spam filters. In this paper we show that lazy learning techniques are appropriate for such dynamically changing contexts. We present a case-based system for spam filtering called ECUE that can learn dynamically. We evaluate its performance as the case-base is updated with new cases. We also explore the benefit of periodically redoing the feature selection process to bring new features into play. Our evaluation shows that these two levels of model update are effective in tracking concept drift.
http://hdl.handle.net/2262/13242
Marked
Mark
Trading Privacy for Trust
(2004)
Seigneur, Jean-Marc; Jensen, Christian D.
Trading Privacy for Trust
(2004)
Seigneur, Jean-Marc; Jensen, Christian D.
Abstract:
TCD-CS-2004-38
Both privacy and trust relate to knowledge about an entity. However, there is an inherent conflict between trust and privacy: the more knowledge a first entity knows about a second entity, the more accurate should be the trustworthiness assessment; the more knowledge is known about this second entity, the less privacy is left to this entity. This conflict needs to be addressed because both trust and privacy are essential elements for a smart working world. The solution should allow the benefit of adjunct trust when entities interact without too much privacy loss. We propose to achieve the right trade-off between trust and privacy by ensuring minimal trade of privacy for the required trust. We demonstrate how transactions made under different pseudonyms can be linked and careful disclosure of such links fulfils this right trade-off.
http://hdl.handle.net/2262/13240
Marked
Mark
An Assessment of Case-Based Reasoning for Spam Filtering
(2004)
Delany, Sarah Jane; Cunningham, Padraig; Coyle, Lorcan
An Assessment of Case-Based Reasoning for Spam Filtering
(2004)
Delany, Sarah Jane; Cunningham, Padraig; Coyle, Lorcan
Abstract:
TCD-CS-2004-44
Because of the changing nature of spam, a spam filtering system that uses machine learning will need to be dynamic. This suggests that a case-based (memory-based) approach may work well. Case-Based Reasoning (CBR) is a lazy approach to machine learning where induction is delayed to run time. This means that the case base can be updated continuously and new training data is immediately available to the induction process. In this paper we present a detailed description of such a system called ECUE and evaluate design decisions concerning the case representation. We compare its performance with an alternative system that uses Naive Bayes (NB). We find that there is little to choose between the two alternatives in cross-validation tests on data sets. However, ECUE does appear to have some advantages in tracking concept drift over time.
http://hdl.handle.net/2262/13238
Marked
Mark
Gaining Insight through Case-Based Explanation
(2004)
Nugent, Conor; Doyle, Donal; Cunningham, Padraig
Gaining Insight through Case-Based Explanation
(2004)
Nugent, Conor; Doyle, Donal; Cunningham, Padraig
Abstract:
TCD-CS-2004-49
Because CBR is an interpretable process, it is a reasoning mechanism that supports explanation. This can be done explicitly by the system designers incorporating explanation patterns in cases. This can be termed knowledge-intensive explanation in CBR. However, of more interest here is case-based explanation that works by allowing users to consider the relation between different cases. The recommendation of a decision support system can be explained by presenting similar cases that motivate the recommendation. Users can derive insight from similar cases that have different outcomes. The differences in outcome are due to the differences in the un-matching features (provided the effect is not due to noisy data). This is a more knowledge-light approach to case-based explanation. This is appropriate for weak-theory domains where the details of the causal interactions in the domain are not well understood; experts would however be able to express the direction of causal...
http://hdl.handle.net/2262/13218
Marked
Mark
Programme driven music radio
(2002)
Hayes, Conor; Cunningham, Padraig; Clerkin, Patrick; Grimaldi, Marco
Programme driven music radio
(2002)
Hayes, Conor; Cunningham, Padraig; Clerkin, Patrick; Grimaldi, Marco
Abstract:
TCD-CS-2002-07
This paper describes the operation of and research behind a networked application for the delivery of personalised streams of music at Trinity College Dublin. Smart Radio is a web based client-server application that uses streaming audio technology and recommendation techniques to allow users build, manage and share music programmes. While it is generally acknowledged that music distribution over the web will dramatically change how the music industry operates, there are few prototypes available to demonstrate how this could work in a regulated way. The Smart Radio approach is to have people manage their music resources by putting together personalised music programmes. These programmes can then be recommended to other listeners using a combination of collaborative and contentbased recommendation strategies. We describe how we use a novel two-stage approach to find recommendations that are pertinent to a listener?s current listening preferences, something which co...
http://hdl.handle.net/2262/13198
Marked
Mark
An on-line evaluation framework for recommender systems
(2002)
Hayes, Conor; Cunningham, Padraig
An on-line evaluation framework for recommender systems
(2002)
Hayes, Conor; Cunningham, Padraig
Abstract:
TCD-CS-2002-19
Several techniques are currently used to evaluate recommender systems. These techniques involve off-line analysis using evaluation methods from machine learning and information retrieval. We argue that while off-line analysis is useful, user satisfaction with a recommendation strategy can only be measured in an on-line context. We propose a new evaluation framework which involves a paired test of two recommender systems which simultaneously compete to give the best recommendations to the same user at the same time. The user interface and the interaction model for each system is the same. The framework enables you to specify an API so that different recommendation strategies may take part in such a competition. The API defines issues such as access to data, the interaction model and the means of gathering positive feedback from the user. In this way it is possible to obtain a relative measure of user satisfaction with the two systems.
http://hdl.handle.net/2262/13178
Marked
Mark
A Case-Based Personal Travel Assistant for Elaborating User Requirements and Assessing Offers
(2002)
Coyle, Lorcan; Cunningham, Padraig; Hayes, Conor
A Case-Based Personal Travel Assistant for Elaborating User Requirements and Assessing Offers
(2002)
Coyle, Lorcan; Cunningham, Padraig; Hayes, Conor
Abstract:
TCD-CS-2002-17
This paper describes a case-based approach to user profiling in a Personal Travel assistant (based on the 1998 FIPA Travel Scenario). The ap-proach is novel in that the user profile is made up of a set of cases capturing previous interactions rather than as a single composite case. This has the advan-tage that the profile is always up-to-date and also allows for the borrowing of cases from similar users when coverage is poor. Profile data is retrieved from a database in an XML format and loaded into a case-retrieval net in memory. This case-retrieval net is then used to support the two key tasks of requirements elaboration and ranking offers.
http://hdl.handle.net/2262/13179
Marked
Mark
An Approach to Aggregating Ensembles of Lazy Learners that Supports Explanation
(2002)
Zenobi, Gabriele; Cunningham, Padraig
An Approach to Aggregating Ensembles of Lazy Learners that Supports Explanation
(2002)
Zenobi, Gabriele; Cunningham, Padraig
Abstract:
TCD-CS-2002-20
Ensemble research has shown that the aggregated output of an ensemble of predictors can be more accurate than a single predictor. This is true also for lazy learning systems like Case-Based Reasoning (CBR) and k-Nearest- Neighbour. Aggregation is normally achieved by voting in classification tasks and by averaging in regression tasks. For CBR, this increased accuracy comes at the cost of interpretability however. If we consider the use of retrieved cases for explanation to be one of the advantages of CBR then this is lost in an ensemble. This is because a large number of cases will have been retrieved by the ensemble members. In this paper we present a new technique for aggregation that obtains excellent results and identifies a small number of cases for use in explanation. This new approach might be viewed as a transformation process whereby cases are transformed from their feature based representation to a representation based on the predictions of ensemble memb...
http://hdl.handle.net/2262/13160
Marked
Mark
Ontology Discovery for the Semantic Web Using Hierarchical Clustering
(2002)
Clerkin, Patrick; Cunningham, Padraig; Hayes, Conor
Ontology Discovery for the Semantic Web Using Hierarchical Clustering
(2002)
Clerkin, Patrick; Cunningham, Padraig; Hayes, Conor
Abstract:
TCD-CS-2002-25
According to a proposal by Tim Berners-Lee, the World Wide Web should be extended to make a Semantic Web where human understandable content is structured in such a way as to make it machine processable. Central to this conception is the establishment of shared ontologies, which specify the fundamental objects and relations important to particular online communities. Normally, such ontologies are hand crafted by domain experts. In this paper we propose that certain techniques employed in data mining tasks can be adopted to automatically discover and generate ontologies. In particular, we focus on the conceptual clustering algorithm, COBWEB, and show that it can be used to generate class hierarchies expressible in RDF Schema. We consider applications of this approach to online communities where recommendation of assets on the basis of user behaviour is the goal, illustrating our arguments with reference to the Smart Ra...
http://hdl.handle.net/2262/13158
Marked
Mark
Automated Case Generation for Recommender Systems Using Knowledge Discovery Techniques
(2002)
Clerkin, Patrick; Hayes, Conor; Cunningham, Padraig
Automated Case Generation for Recommender Systems Using Knowledge Discovery Techniques
(2002)
Clerkin, Patrick; Hayes, Conor; Cunningham, Padraig
Abstract:
TCD-CS-2002-24
One approach to product recommendation in ecommerce is collaborative filtering, which is based on data of users? consumption of assets. The alternative case-based approach is based on a more semantically rich representation of users and assets. However, generating these case representations can be a significant overhead in system development. In this paper we present an approach to case authoring based on data mining methods. Specifically, we focus on clustering algorithms. Having demonstrated the feasibility of this approach, we go on to consider what benefits such techniques might confer on the recommendation system. In this context we distinguish three levels of interpretability of cluster formations or concepts, and go on to argue that, while the first two levels offer no immediate advantages over each other in the recommendation domain, moving to the third level allows us to overcome the bootstrap problem of recommending assets to new users.
http://hdl.handle.net/2262/13159
Marked
Mark
Discovering Genome Expression Patterns With Self-Organizing Neural Networks
(2002)
Azuaje, Francisco
Discovering Genome Expression Patterns With Self-Organizing Neural Networks
(2002)
Azuaje, Francisco
Abstract:
TCD-CS-2002-30
http://hdl.handle.net/2262/13143
Marked
Mark
Solutions to Instability Problems with Sequential Wrapper-based Approaches to Feature Selection
(2002)
Dunne, Kevin; Cunningham, Padraig; Azuaje, Francisco
Solutions to Instability Problems with Sequential Wrapper-based Approaches to Feature Selection
(2002)
Dunne, Kevin; Cunningham, Padraig; Azuaje, Francisco
Abstract:
TCD-CS-2002-28
It is generally accepted that Wrapper approaches will outperform Filter-based approaches to feature selection, particularly in situations where an adequate amount of data is available. What is often overlooked is that Wrapper approaches can be unstable. For instance, different partitionings of the training data can result in di#11;erent routes through the search space and thus in di#11;erent feature subsets being selected. In this paper we illustrate examples of this problem and a solution based on the aggregation of several runs of a sequential search is suggested. This is essentially an ensemble solution to instability in feature subset selection and it does seem to stabilise the process.
http://hdl.handle.net/2262/13144
Marked
Mark
Representing Cases for CBR in XML
(2002)
Coyle, Lorcan; Hayes, Conor; Cunningham, Padraig
Representing Cases for CBR in XML
(2002)
Coyle, Lorcan; Hayes, Conor; Cunningham, Padraig
Abstract:
TCD-CS-2002-63
Case Based Reasoning has found increasing application on the Internet as a shopping assistant for e-commerce stores. The strength of CBR in this area stems from its reuse of the knowledge base associated with a particular application, thus providing an ideal way to make personalised configuration or technical information available to the Internet user. Since case data may be one aspect of a company's entire knowledge system, it is important to integrate case data easily within a company's IT infrastructure, providing in effect a case-based view on relevant portions of the company knowledge base. We describe CBML, an XML-based Case Mark-Up Language we have developed to facilitate such integration. We will detail the benefits of our system for industry in general in terms of extensibility, ease of reuse and interoperability. The language allows us to make the formal definition of the structure of our cases completely independent of the application code. In...
http://hdl.handle.net/2262/13141
Marked
Mark
Overfitting and Diversity in Classification Ensembles based on Feature Selection
(2000)
Cunningham, Padraig
Overfitting and Diversity in Classification Ensembles based on Feature Selection
(2000)
Cunningham, Padraig
Abstract:
TCD-CS-2000-07
This paper addresses Wrapper-like approaches to feature subset selection and the production of classifier ensembles based on members with different feature subsets. The paper starts with the observation that if an insufficient amount of data is used to guide the Wrapper search then the feature selection will overfit the data. If the objective of the feature selection exercise is to build a better predictor, rather than identify important features for data mining reasons, then ensembles offers a solution. Overfitting may be used to provide diversity in ensembles provided the overfitted members have variety. The paper concludes with an assessment of entropy as a measure of diversity in classifier ensembles. A tentative conclusion is that diversity is not such a problem where a large number of features is involved but needs to be monitored for problems with smaller numbers of features ? say less than 25.
http://hdl.handle.net/2262/13078
Marked
Mark
Java Decaffeinated: experiences building a programming language from components
(2000)
Farragher, Linda; Dobson, Simon
Java Decaffeinated: experiences building a programming language from components
(2000)
Farragher, Linda; Dobson, Simon
Abstract:
TCD-CS-2000-22
Most modern programming languages are complex and feature rich. Whilst this is (sometimes) an advantage for industrial-strength applications, it complicates both language teaching and language research. We describe our experiences in the design of a reduced sub-set of the Java language and its implementation using the Vanilla language development framework. We argue that Vanilla?s component-based approach allows the language?s feature set to be varied quickly and simply compared with other development approaches.
http://hdl.handle.net/2262/13043
Marked
Mark
A Dynamic Approach to Reducing Dialog in On-Line Decision Guides
(2000)
Doyle, Michelle; Cunningham, Padraig
A Dynamic Approach to Reducing Dialog in On-Line Decision Guides
(2000)
Doyle, Michelle; Cunningham, Padraig
Abstract:
TCD-CS-2000-14
Online decision guides typically ask too many questions of the user, as they make no attempt to focus the questions. We describe some approaches to minimising the questions asked of a user in an online query situation. Questions are asked in an order that reflects their ability to narrow down the set of cases. Thus time to reach an answer is decreased. This has the dual benefit of taking some of the monotony out of online queries, and also of decreasing the amount of network request-response cycles. Most importantly, question order is decided at run time, and therefore adapts to the user. This approach is in the spirit of lazy learning with induction delayed to run-time, allowing adaptation to the emerging details of the situation. We evaluate a few different approaches to the question selection task, and compare the best approach (one based on ideas from retrieval in CBR) to a commercial online decision guide.
http://hdl.handle.net/2262/13058
Marked
Mark
Adaptability in CORBA: The Mobile Proxy Approach
(2000)
Aziz, Benjamin; Jensen, Christian D.
Adaptability in CORBA: The Mobile Proxy Approach
(2000)
Aziz, Benjamin; Jensen, Christian D.
Abstract:
TCD-CS-2000-57
Adaptability is one of the most important challenges in modern distributed systems. It may be defined as the ease with which a software application satisfies the different system constraints and the requirements of users and other applications. Adaptability is needed because distributed systems are inherently open, heterogeneous, and dynamic environments integrating a wide range of platforms, operating systems and applications from a number of different sources. In this paper, we propose to use mobile proxies to provide adaptability in distributed applications integrated using the CORBA technology. Downloading stubs and skeletons at runtime allows the adaptation of either client or server interfaces as well as the protocol linking the two.
http://hdl.handle.net/2262/13041
Displaying Results 126 - 150 of 3088 on page 6 of 124
1
2
3
4
5
6
7
8
9
10
11
Bibtex
CSV
EndNote
RefWorks
RIS
XML
Item Type
Book (3)
Book chapter (212)
Conference item (1165)
Contribution to newspaper/m... (1)
Doctoral thesis (217)
Journal article (745)
Master thesis (research) (52)
Master thesis (taught) (79)
Report (215)
Review (2)
Working paper (9)
Other (388)
Institution
Dublin City University (175)
NUI Galway (114)
Maynooth University (808)
Trinity College Dublin (859)
University College Cork (137)
University College Dublin (206)
University of Limerick (519)
Dublin Institute of Technology (128)
Lenus (2)
Dundalk Institute of Techno... (129)
Marine Institute (1)
Teagasc (2)
Royal College of Surgeons i... (5)
Connacht-Ulster Alliance (3)
Peer Review Status
Peer reviewed (1898)
Non peer reviewed (282)
Unknown (908)
Year
2021 (16)
2020 (87)
2019 (113)
2018 (91)
2017 (99)
2016 (102)
2015 (71)
2014 (131)
2013 (154)
2012 (231)
2011 (225)
2010 (233)
2009 (304)
2008 (150)
2007 (157)
2006 (160)
2005 (169)
2004 (126)
2003 (83)
2002 (69)
2001 (53)
2000 (43)
1999 (50)
1998 (24)
1997 (22)
built by Enovation Solutions