— The paper proposes a hypernetwork-based method for stock market prediction through a binary time series problem. Hypernetworks are a random hypergraph structure of higher-order...
Elena Bautu, Sun Kim, Andrei Bautu, Henri Luchian,...
Most accurate predictions are typically obtained by learning machines with complex feature spaces (as e.g. induced by kernels). Unfortunately, such decision rules are hardly access...
We provide a systematic transformation of an LL(1) grammar to an object model that consists of · an object structure representing the non-terminal symbols and their corresponding...
Background: Expression array data are used to predict biological functions of uncharacterized genes by comparing their expression profiles to those of characterized genes. While b...
A hybrid neuro-symbolic problem-solving model is presented in which the aim is to forecast parameters of a complex and dynamic environment in an unsupervised way. In situations in ...