By Hiroshi Sakai, Mao Wu, Michinori Nakata, Dominik Ślęzak (auth.), Aboul Ella Hassanien, Abdel-Badeeh M. Salem, Rabie Ramadan, Tai-hoon Kim (eds.)
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Additional resources for Advanced Machine Learning Technologies and Applications: First International Conference, AMLTA 2012, Cairo, Egypt, December 8-10, 2012. Proceedings
It also provides new intuition with regard to algorithms developed for searching for optimal feature subsets and interpretation of classiﬁers that are based on such subsets obtained using various criteria for determining decisions. Thanks to the above equivalence, we have a free choice which measure to use for calculation. , random cut generation. This part of our future research is closely related to the topic of scalability of rough-set-based algorithms, which is particularly challenging with respect to fast heuristic evaluation of subsets of numeric attributes [14,15].
The input operator opIn belongs to one of the classes: t-norms or s-norms. It concerns the way in which all input places are connected to a given transition t (more precisely, statements corresponding to those places). Moreover, the output operator: opOut1 belongs to the class of t-norms and opOut2 belongs to the class of s-norms. Both of them concern the way in which the marking is computed after ﬁring the transition t. This issue is explained more precisely below. The generalised fuzzy Petri net dynamics deﬁnes how new markings are computed from the current marking when transitions are ﬁred (the corresponding state change occurs).
Usually, this number is interpreted as a truth degree of an implication corresponding to a given transition t ,. The meaning of the threshold function γ is explained below. The operator binding function δ connects transitions with triples of operators (opIn , opOut1 , opOut2 ). The ﬁrst operator appearing in this triple is called the input operator, and two remaining ones are called the output operators. The input operator opIn belongs to one of the classes: t-norms or s-norms. It concerns the way in which all input places are connected to a given transition t (more precisely, statements corresponding to those places).
Advanced Machine Learning Technologies and Applications: First International Conference, AMLTA 2012, Cairo, Egypt, December 8-10, 2012. Proceedings by Hiroshi Sakai, Mao Wu, Michinori Nakata, Dominik Ślęzak (auth.), Aboul Ella Hassanien, Abdel-Badeeh M. Salem, Rabie Ramadan, Tai-hoon Kim (eds.)