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Representation does matter

Bibliographic Details
Title: Representation does matter
Authors: Abela, John, 5th Computer Science Annual Workshop (CSAW’07)
Publisher Information: University of Malta. Faculty of ICT
Publication Year: 2007
Collection: University of Malta: OAR@UM / L-Università ta' Malta
Subject Terms: Machine learning -- Development, Representations of categories, Computer algorithms, Induction (Mathematics) -- Computer programs
Description: In Machine Learning the main problem is that of learning a ‘description’ of a class (possibly an infinite set) from a finite num- ber of positive and negative training examples. For real world problems, however, one must distinguish between the actual instance of the class to be learned and the numeric or symbolic encoding of the instances of the same class. The question here is whether different encodings (or representations) of the instances of a real-world class can actually affect the performance of the learning algorithm. In artificial neural networks (ANNs), for example, it is required that the classes are always encoded as vectors over some field (usually the set of reals). In this paper it is argued that the representation of the class instances plays a very important role in machine learning since it has bearing on two very important issues — the structural completeness of the training set and also the inductive bias of the learning algorithm. ; peer-reviewed
Document Type: conference object
Language: English
Relation: https://www.um.edu.mt/library/oar//handle/123456789/22566
Availability: https://www.um.edu.mt/library/oar//handle/123456789/22566
Rights: info:eu-repo/semantics/openAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.
Accession Number: edsbas.FBC890F6
Database: BASE
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  Data: Representation does matter
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  Data: <searchLink fieldCode="AR" term="%22Abela%2C+John%22">Abela, John</searchLink><br /><searchLink fieldCode="AR" term="%225th+Computer+Science+Annual+Workshop+%28CSAW%2707%29%22">5th Computer Science Annual Workshop (CSAW’07)</searchLink>
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  Data: University of Malta. Faculty of ICT
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  Data: 2007
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  Data: <searchLink fieldCode="DE" term="%22Machine+learning+--+Development%22">Machine learning -- Development</searchLink><br /><searchLink fieldCode="DE" term="%22Representations+of+categories%22">Representations of categories</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+algorithms%22">Computer algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Induction+%28Mathematics%29+--+Computer+programs%22">Induction (Mathematics) -- Computer programs</searchLink>
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  Data: In Machine Learning the main problem is that of learning a ‘description’ of a class (possibly an infinite set) from a finite num- ber of positive and negative training examples. For real world problems, however, one must distinguish between the actual instance of the class to be learned and the numeric or symbolic encoding of the instances of the same class. The question here is whether different encodings (or representations) of the instances of a real-world class can actually affect the performance of the learning algorithm. In artificial neural networks (ANNs), for example, it is required that the classes are always encoded as vectors over some field (usually the set of reals). In this paper it is argued that the representation of the class instances plays a very important role in machine learning since it has bearing on two very important issues — the structural completeness of the training set and also the inductive bias of the learning algorithm. ; peer-reviewed
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      – Text: English
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      – SubjectFull: Representations of categories
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      – SubjectFull: Computer algorithms
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