Design And Analysis Of Learning Classifier Systems: A Probabilistic Approach (studies In Computational Intelligence)
by Jan Drugowitsch /
2008 / English / PDF
26 MB Download
This book provides a comprehensive introduction to the design and
analysis of Learning Classifier Systems (LCS) from the
perspective of machine learning. LCS are a family of methods for
handling unsupervised learning, supervised learning and
sequential decision tasks by decomposing larger problem spaces
into easy-to-handle subproblems. Contrary to commonly approaching
their design and analysis from the viewpoint of evolutionary
computation, this book instead promotes a probabilistic
model-based approach, based on their defining question "What is
an LCS supposed to learn?". Systematically following this
approach, it is shown how generic machine learning methods can be
applied to design LCS algorithms from the first principles of
their underlying probabilistic model, which is in this book – for
illustrative purposes – closely related to the currently
prominent XCS classifier system. The approach is holistic in the
sense that the uniform goal-driven design metaphor essentially
covers all aspects of LCS and puts them on a solid foundation, in
addition to enabling the transfer of the theoretical foundation
of the various applied machine learning methods onto LCS. Thus,
it does not only advance the analysis of existing LCS but also
puts forward the design of new LCS within that same framework.
This book provides a comprehensive introduction to the design and
analysis of Learning Classifier Systems (LCS) from the
perspective of machine learning. LCS are a family of methods for
handling unsupervised learning, supervised learning and
sequential decision tasks by decomposing larger problem spaces
into easy-to-handle subproblems. Contrary to commonly approaching
their design and analysis from the viewpoint of evolutionary
computation, this book instead promotes a probabilistic
model-based approach, based on their defining question "What is
an LCS supposed to learn?". Systematically following this
approach, it is shown how generic machine learning methods can be
applied to design LCS algorithms from the first principles of
their underlying probabilistic model, which is in this book – for
illustrative purposes – closely related to the currently
prominent XCS classifier system. The approach is holistic in the
sense that the uniform goal-driven design metaphor essentially
covers all aspects of LCS and puts them on a solid foundation, in
addition to enabling the transfer of the theoretical foundation
of the various applied machine learning methods onto LCS. Thus,
it does not only advance the analysis of existing LCS but also
puts forward the design of new LCS within that same framework.