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Measuring and repairing inconsistency in probabilistic knowledge bases
The computation of the family of measures presented here, in as much as it yields an adjustment in the probability of each statement that restores consistency, provides the modeler with possible repairs of the knowledge base. Expand
Finding neural assemblies with frequent item set mining
This work proposes a new assembly detection method based on frequent item set mining (FIM), which is able to reliably suppress false discoveries, while it is still very sensitive in discovering synchronous activity. Expand
Inconsistency as qualified truth: A probability logic approach
We treat the sentences in a finite inconsistent knowledge base as assertions that are true with probability at least some primary threshold @h and consider as consequences those assertions entailedExpand
Information from Inconsistent Knowledge: A Probability Logic Approach
We treat the sentences in a finite inconsistent knowledge base as assertions that are true with probability at least some primary threshold η and consider as consequences those assertions entailed toExpand
Simple Pattern Spectrum Estimation for Fast Pattern Filtering with CoCoNAD
This work proposes a simple estimation method, with which (an approximation of) a pattern spectrum can be derived from the original data, bypassing the time-consuming generation and analysis of surrogate data sets. Expand
Statistical evaluation of synchronous spike patterns extracted by frequent item set mining
By employing stochastic simulations of parallel spike trains with correlated activity in form of injected spike synchrony in subsets of the neurons, it is demonstrated for a range of parameter settings that the analysis scheme composed of FIM, PSF and PSR allows to reliably detect active assemblies in massively Parallel spike trains. Expand
Formal approaches to rule-based systems in medicine: The case of CADIAG-2
Three mathematical models that have been recently proposed by the authors for CADIAG-2, a well-known system of this kind, are discussed, based on fuzzy logics, probability theory and possibilistic logic, respectively. Expand
Fuzzy Frequent Pattern Mining in Spike Trains
We present a framework for characterizing spike (and spike-train) synchrony in parallel neuronal spike trains that is based on identifying spikes with what we call influence maps: real-valuedExpand
Frequent item set mining for sequential data: Synchrony in neuronal spike trains
A framework for characterizing spike synchrony in neuronal spike-train recordings is presented that is based on the identification of spikes with what are called influence maps: real-valued functions that describe an influence region around the corresponding spike times within which a continuous and possibly graded notion of synchrony among spikes is defined. Expand
Finding Frequent Patterns in Parallel Point Processes
This work defines the support of an item set in this setting based on a maximum independent set approach allowing for efficient computation and shows how the enumeration and test of candidate sets can be made efficient by properly reducing the event sequences and exploiting perfect extension pruning. Expand