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Combining numeric and symbolic learning techniques
Incremental learning from examples in a noisy domain is a difficult problem in Machine Learning. In this paper we divide the task into two subproblems and present a combination of numeric and symbolic approaches that yields robust learning of boolean characterizations. Our method has been implemented in a computer program, and we plot its empirical learning performance in the presence of varying amounts of noise
Quantum Structure in Cognition, Origins, Developments, Successes and Expectations
We provide an overview of the results we have attained in the last decade on
the identification of quantum structures in cognition and, more specifically,
in the formalization and representation of natural concepts. We firstly discuss
the quantum foundational reasons that led us to investigate the mechanisms of
formation and combination of concepts in human reasoning, starting from the
empirically observed deviations from classical logical and probabilistic
structures. We then develop our quantum-theoretic perspective in Fock space
which allows successful modeling of various sets of cognitive experiments
collected by different scientists, including ourselves. In addition, we
formulate a unified explanatory hypothesis for the presence of quantum
structures in cognitive processes, and discuss our recent discovery of further
quantum aspects in concept combinations, namely, 'entanglement' and
'indistinguishability'. We finally illustrate perspectives for future research.Comment: 25 pages. arXiv admin note: text overlap with arXiv:1412.870
An approach for uncertainty aggregation using generalised conjunction/disjunction aggregators
Decision Support Systems are often used in the area of system evaluation. The quality of the output of such a system is only as good as the quality of the data that is used as input. Uncertainty on data, if not taken into account, can lead to evaluation results that are not representative. In this paper, we propose a technique to extend Generalised Con- junction/Disjunction aggregators to deal with un- certainty in Decision Support Systems. We first de- fine the logic properties of uncertainty aggregation through reasoning on strict aggregators and after- wards extend this logic to partial aggregators
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