Overview
- Editors:
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Max Bramer
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Dept. Computer Science and, University of Portsmouth, Portsmouth, United Kingdom
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Richard Ellis
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Stratum Management Ltd., Micheldever, Hants., United Kingdom
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Miltos Petridis
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School of Computing &, University of Greenwich, London, United Kingdom
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Table of contents (40 papers)
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Research and Development in Intelligent Systems XXVI
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- Pierre-Antoine Champin, Peter Briggs, Maurice Coyle, Barry Smyth
Pages 5-18
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Knowledge Discovery And Data Mining
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KNOWLEDGE DISCOVERY AND DATA MINING
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- Chuntao Jiang, Frans Coenen, Robert Sanderson, Michele Zito
Pages 21-34
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- M. Sulaiman Khan, F. Coenen, D. Reid, R. Patel, L. Archer
Pages 35-48
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- Michael P. O’Mahony, Barry Smyth
Pages 49-62
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- Laura Moss, Derek Sleeman, Malcolm Sim, Malcolm Booth, Malcolm Daniel, Lyndsay Donaldson et al.
Pages 63-76
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REASONING
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- John Debenham, Carles Sierra
Pages 79-92
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- Baher El-Geresy, Alia I. Abdelmoty, Andrew J. Ware
Pages 93-106
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- Henri Prade, Gilles Richard
Pages 121-134
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Data Mining And Machine Learning
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Front Matter
Pages 135-135
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DATA MINING AND MACHINE LEARNING
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- Christopher Fogelberg, Vasile Palade
Pages 137-149
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- Frederic Stahl, Max Bramer, Mo Adda
Pages 151-164
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- Guillermo Nebot-Troyano, Lluís A. Belanche-Muñoz
Pages 165-178
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- Karthikeyan Natesan Ramamurthy, Jayaraman J. Thiagarajan, Andreas Spanias
Pages 179-192
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Optimisation And Planning
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Front Matter
Pages 193-193
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OPTIMISATION AND PLANNING
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About this book
The most common document formalisation for text classi?cation is the vector space model founded on the bag of words/phrases representation. The main advantage of the vector space model is that it can readily be employed by classi?cation - gorithms. However, the bag of words/phrases representation is suited to capturing only word/phrase frequency; structural and semantic information is ignored. It has been established that structural information plays an important role in classi?cation accuracy [14]. An alternative to the bag of words/phrases representation is a graph based rep- sentation, which intuitively possesses much more expressive power. However, this representation introduces an additional level of complexity in that the calculation of the similarity between two graphs is signi?cantly more computationally expensive than between two vectors (see for example [16]). Some work (see for example [12]) has been done on hybrid representations to capture both structural elements (- ing the graph model) and signi?cant features using the vector model. However the computational resources required to process this hybrid model are still extensive.
Reviews
From the reviews:
“Papers and posters presented at the 29th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence … are collected in this book. It presents a wide range of research topics. … Most of the papers have a quite formal approach. … They are all presented to professionals in AI, so the book should appeal to AI scholars and practitioners.” (G. Gini, ACM Computing Reviews, April, 2010)
Editors and Affiliations
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Dept. Computer Science and, University of Portsmouth, Portsmouth, United Kingdom
Max Bramer
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Stratum Management Ltd., Micheldever, Hants., United Kingdom
Richard Ellis
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School of Computing &, University of Greenwich, London, United Kingdom
Miltos Petridis
