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The ability of a chosen classification algorithm to
induce a good generalization
depends on how appropriate its representation
language used to express
generalizations of the examples is for the given
task.
Since different learning algorithms employ different
knowledge
representations and search heuristics,
different search spaces are explored and diverse
results are obtained.
The problem of finding the appropriate model for a
given task is an active research area.
In this dissertation, instead of looking for methods
that fit the data using
a single representation language, we present a
family of algorithms,
under the generic name of {em Cascade
Generalization},
whose search spaces contains models that use
different representation languages.
induce a good generalization
depends on how appropriate its representation
language used to express
generalizations of the examples is for the given
task.
Since different learning algorithms employ different
knowledge
representations and search heuristics,
different search spaces are explored and diverse
results are obtained.
The problem of finding the appropriate model for a
given task is an active research area.
In this dissertation, instead of looking for methods
that fit the data using
a single representation language, we present a
family of algorithms,
under the generic name of {em Cascade
Generalization},
whose search spaces contains models that use
different representation languages.