General
Objectives |
LINNEO+ is a knowledge acquisition tool and it works incrementally
with an Unsupervised learning strategy that accepts a stream of observations
and discovers a classification scheme over the data stream. As a control
strategy it retains only the best hypotheses that are consistent with the
observations given a similarity criterion.
LINNEO+ uses information about the domain elements to induce
classes and takes advantage of domain theory (a priori knowledge) which
represents the expert's current state of knowledge (which may be incomplete).
This theory constrains the possible outcomes of the classification process
semantically biasing the results.
The human expert abstracts a collection of observations and a set of
attributes that he thinks of as relevant . At the same time, the expert
is also able to express what he already knows about the domain. This knowledge
(that will be called Domain Theory (DT)) is represented as set of rules.
Starting with this knowledge and data, LINNEO uses induction over the observations
to generate a classification (see [Béjar95]).
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The
KEMLg-LSI Contribution to the project |
The LINNEO+ tool has been fully developed inside our group. |
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References |
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[Béjar95]
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J. Béjar, Adquisición de conocimiento en dominios poco
estructurados PhD thesis, Departament de Llenguatges i Sistemes Informàtics.
Facultat d'Informatica de Barcelona. Universitat Politecnica de Catalunya,
1995.
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[Béjar94]
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J. Béjar, U. Cortés,and M. Domingo, ``Using
domain theory to bias classification processes in ill-domains'', in
proceedings of the IV congreso Iberoamericano de Inteligencia Artificial
(IBERAMIA '94), pp. 187-197, 1994.
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Entities
involved in the project |
The
KEMLg members involved |
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