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Books > Computing & IT > General theory of computing > Systems analysis & design
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Abstraction, Aggregation and Recursion for Accurate and Simple Classifiers (Paperback)
Loot Price: R1,459
Discovery Miles 14 590
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Abstraction, Aggregation and Recursion for Accurate and Simple Classifiers (Paperback)
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Total price: R1,479
Discovery Miles: 14 790
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In a typical inductive learning scenario, instances in a data set
are simply represented as ordered tuples of attribute values. In my
research, I explore three methodologies to improve the accuracy and
compactness of the classifiers: abstraction, aggregation, and
recursion. Firstly, abstraction is aimed at the design and analysis
of algorithms that generate and deal with taxonomies for the
construction of compact and robust classifiers. Secondly, I apply
aggregation method to constructively invent features in a multiset
representation for classification tasks. Finally, I construct a set
of classifiers by recursive application of weak learning
algorithms. Experimental results on various benchmark data sets
indicate that the proposed methodologies are useful in constructing
simpler and more accurate classifiers.
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