RESEARCH AND DEVELOPMENT IN INTELLIGENT SYSTEMS XXVI: INCORPORATING APPLICATIONS AND INNOVATIONS IN INTELLIGENT SYSTEMS XVII
ELLIS,R., PETRIDIS,M.
ISBN 10: 1848829825 ISBN 13: 9781848829824
Editorial: Springer, 2009
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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.
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RESEARCH AND DEVELOPMENT IN INTELLIGENT SYSTEMS XXVI
INCORPORATING APPLICATIONS AND INNOVATIONS IN INTELLIGENT SYSTEMS XVII
The papers in this volume are the refereed papers presented at AI-2009, the Twenty-ninth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, held in Cambridge in December 2009 in both the technical and the application streams.
They present new and innovative developments and applications, divided into technical stream sections on Knowledge Discovery and Data Mining, Reasoning, Data Mining and Machine Learning, Optimisation and Planning, and Knowledge Acquisition and Evolutionary Computation, followed by application stream sections on AI and Design, Commercial Applications of AI and Further AI Applications. The volume also includes the text of short papers presented as posters at the conference.
This is the twenty-sixth volume in the Research and Development in Intelligent Systems series, which also incorporates the seventeenth volume in the Applications and Innovations in Intelligent Systems series. These series are essential reading for those who wish to keep up to date with developments in this important field.
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Título: RESEARCH AND DEVELOPMENT IN INTELLIGENT ...
Editorial: Springer
Año de publicación: 2009
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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,. Nº de ref. del artículo: 4287169
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Research and Development in Intelligent Systems XXVI
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 504 pp. Englisch. Nº de ref. del artículo: 9781848829824
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9781848829824
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