By John Case, Dayanand S. Rajan, Anil M. Shende (auth.), Klaus P. Jantke (eds.)
This quantity includes the textual content of the 5 invited papers and sixteen chosen contributions awarded on the 3rd overseas Workshop on Analogical and Inductive Inference, AII `92, held in Dagstuhl fortress, Germany, October 5-9, 1992. just like the past occasions, AII '92 used to be meant to collect representatives from a number of study groups, specifically, from theoretical computing device technological know-how, synthetic intelligence, and from cognitive sciences. The papers contained during this quantity represent a cutting-edge record on formal methods to algorithmic studying, rather emphasizing features of analogical reasoning and inductive inference. either those parts are at present attracting powerful curiosity: analogical reasoning performs a vital function within the booming box of case-based reasoning, and, within the fieldof inductive common sense programming, there have lately been constructed a couple of new options for inductive inference.
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Additional info for Analogical and Inductive Inference: International Workshop AII '92 Dagstuhl Castle, Germany, October 5–9, 1992 Proceedings
E. CNN, IB2, IB3, SNN, ENN, DROP1-DROP5, All k-NN). 5) , the parallel-PLAANNs algorithm and cascade-PLAANN algorithm. The third algorithm is based on an artiﬁcial neural network which is trained using an implementation of the population learning algorithm. Possibility of applying population learning algorithms to train ANN has been investigated in earlier paper of the authors . The last algorithm is based on the idea of the Cascade Correlation (CasCor) algorithm proposed by Fahlman . The cascade-PLAANN uses for both — network adjustments and candidate neurons training an implementation of the population learning algorithm .
Blue arcs form the MST. The algorithm considers arcs in ascending order of their weights – if both endpoints of an arc are in the same blue subtree, then the arc becomes red, else it is blue. In order to have a number of blue sub-trees depend on relations between documents a modiﬁcation of the original Kruskal’s algorithm is proposed. Marking of the blue arcs that have endpoints in diﬀerent blue trees are preserved, obtaining separate blue trees. A minimum sub-tree clusterization algorithm considers arcs in the ascending order of their weights – if both endpoints of an arc are in a blue tree, color it red, else color it blue.
Empirical comparison of algorithms dataset default set covering compositional accuracy no. 17 4 Conclusions We present two new algorithms to learn decision rules for web usage mining, set-covering and compositional one. g. transactions on banking accounts, or network intrusion). Our experiments with a real data of an internet shop showed usefulness of the proposed approach. In our future work, we plan to perform a thorough comparison of both approaches. 22 5 Petr Berka et al. Acknowledgements The work presented in the paper is supported by the grant GACR 201/03/1318.
Analogical and Inductive Inference: International Workshop AII '92 Dagstuhl Castle, Germany, October 5–9, 1992 Proceedings by John Case, Dayanand S. Rajan, Anil M. Shende (auth.), Klaus P. Jantke (eds.)