Andere Kunden interessierten sich auch für
![Artificial Unintelligence]( "Artificial Unintelligence")
Artificial Unintelligence13,99 €![The Big Nine]( "The Big Nine")
The Big Nine13,99 €![The Technological Singularity]( "The Technological Singularity")
The Technological Singularity13,99 €![Paradigms of Artificial Intelligence Programming]( "Paradigms of Artificial Intelligence Programming")
Paradigms of Artificial Intelligence Programming74,99 €![What to Think About Machines That Think]( "What to Think About Machines That Think")
What to Think About Machines That Think18,99 €![HBR's 10 Must Reads on AI, Analytics, and the New Machine Age (with bonus article]( "HBR's 10 Must Reads on AI, Analytics, and the New Machine Age (with bonus article")
HBR's 10 Must Reads on AI, Analytics, and the New Machine Age (with bonus article "Why Every Company Needs an Augmented Reality Strategy" by Michael E. Porter and James E. Heppelmann)20,99 €![A Human's Guide to Machine Intelligence]( "A Human's Guide to Machine Intelligence")
A Human's Guide to Machine Intelligence18,99 €
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.