Titlebook: Advances in Artificial Intelligence; 15th Conference of t Concha Bielza,Antonio Salmerón,Juan M. Corchado Conference proceedings 2013 Sprin

synovium

Phonophobia

Miriam Focaccia,Raffaella Similieighted means and OWA operators. To do this we take into account that weighted means and OWA operators are particular cases of Choquet integrals. So, the capacities associated to SUOWA operators are defined by using the values of the capacities associated to these functions and idempotent semi-unino

Interregnum

唤醒

Swedenborg and Localization Theoryrather than precise quantitative information. An approach in this field is Order-of-magnitude Reasoning which deals with coarse values of different orders of magnitude which are abstractions of precise values. Several multimodal logics has been introduced to deal with Orders-of-magnitude systems pro

Demonstrate

John Hunter’s Contributions to Neurosciencees of the machine learning community nowadays. Usually the purpose of these methods is to balance the classes. However, there are works in the literature showing that those methods can also be suitable to change the class distribution of not so imbalanced and even balanced databases, to a distributi

GOUGE

The Vision of William Porterfieldhas been the focus of much attention, due to the proliferation of big databases, in some cases distributed across different nodes. However, most of the existing feature selection algorithms were designed for working in a centralized manner, i.e. using the whole dataset at once. In this research, a n

Fillet,Filet

Miriam Focaccia,Raffaella Simili improvements that make the field become very promising. Concretely, HyperNEAT has shown a great potential for evolving large scale neural networks, by discovering geometric regularities, thus being suitable for evolving complex controllers. However, once training phase has finished, evolved neural

细胞膜

希望

宽度

https://doi.org/10.1007/978-81-322-1581-3umptions. One classifier which does that is the semi-naive Bayes. The state-of-the-art algorithm for learning a semi-naive Bayes from data is the backward sequential elimination and joining (BSEJ) algorithm. We extend BSEJ with a second step which removes some of its unwarranted independence assumpt