@article{id:Siminski2012Analysis,
  author = {Krzysztof Simi\'{n}ski},
  title = {Analysis of new method of initialisation of neuro-fuzzy systems with support vector machines},
  doi = {10.2478/v10179-012-0015-2},
  journal = {Theoretical and Applied Informatics},
  year = {2012},
  volume = {24},
  number = {3},
  pages = {243–254},
  month = {November},
  abstract = {The correspondence between support vector machines and neuro-fuzzy
	systems is very interesting. The full equivalence for classification
	and partial for regression has been formally shown. The equivalence
	has very interesting implication. It is a base for a new method of
	initialization of neurofuzzy systems, ie. for creating of fuzzy rule
	base. The commonly used methods are based on reversion of item: the
	premises of fuzzy rules split input domain into region, thus premises
	of fuzzy rules can be elaborated by partition of input domain. This
	leads to three main classes of partition of input domain. The above
	mentioned equivalence results in new way of creating the rule base.
	Now the input domain is not partitioned, but the premises of fuzzy
	rules are extracted from support vector. The objective of the paper
	is to examine the advantages and disadvantages of this new method
	for creation of fuzzy rule bases for neuro-fuzzy systems.},
      note = {[4 pkt]},
}