An argument is made for a double implicational associative information frame, 〈S, , , →, ←〉 being a relevant structure insofar as the specification of the information gain from inference (IGfI) is concerned. A strong informational pluralism is adopted throughout. The rejection of the structural rules K, K′, W, WI, C and CI is justified on the basis of the identification, frequency, and order ...

We deal with the problem how to gain information from many heterogeneous data sources. As a solution we propose a general framework for mediated information gain: it minimizes and supports the human burden, clearly identifies the algorithmically tractable subtasks, and assures a well-understood level of quality. We identify a “mediation task” as an extended amalgamation of searching, querying a...

Despite large incentives, correctness in software remains an elusive goal. Declarative programming techniques, where algorithms are derived from a specification of the desired behavior, offer hope to address this problem, since there is a combinatorial reduction in complexity in programming in terms of specifications instead of algorithms, and arbitrary desired properties can be expressed and e...

In this paper we prove that the reliability of the classifications of individual instances, provided by a classifier, results in information gain with respect to the accuracy of the classifier. We illustrate this result using our new approach to classification reliability called version space support vector machines.