Feature selection via Boolean independent component analysis
Information Sciences: an International Journal
Cultural Specific Effects on the Recognition of Basic Emotions: A Study on Italian Subjects
USAB '09 Proceedings of the 5th Symposium of the Workgroup Human-Computer Interaction and Usability Engineering of the Austrian Computer Society on HCI and Usability for e-Inclusion
Controlling the losing probability in a monotone game
Information Sciences: an International Journal
Neural network-based prediction of cardiovascular response due to the gravitational effects
Control and Intelligent Systems
Playing monotone games to understand learning behaviors
Theoretical Computer Science
COST'11 Proceedings of the 2011 international conference on Cognitive Behavioural Systems
A new goodness-of-fit statistical test
Intelligent Decision Technologies
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With the aim of getting understandable symbolic rules to explain a given phenomenon, we split the task of learning these rules from sensory data in two phases: a multilayer perceptron maps features into propositional variables and a set of subsequent layers operated by a PAC-like algorithm learns Boolean expressions on these variables. The special features of this procedure are that: i) the neural network is trained to produce a Boolean output having the principal task of discriminating between classes of inputs; ii) the symbolic part is directed to compute rules within a family that is not known a priori; iii) the welding point between the two learning systems is represented by a feedback based on a suitability evaluation of the computed rules. The procedure we propose is based on a computational learning paradigm set up recently in some papers in the fields of theoretical computer science, artificial intelligence and cognitive systems. The present article focuses on information management aspects of the procedure. We deal with the lack of prior information about the rules through learning strategies that affect both the meaning of the variables and the description length of the rules into which they combine. The paper uses the task of learning to formally discriminate among several emotional states as both a working example and a test bench for a comparison with previous symbolic and subsymbolic methods in the field.