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Call-by-value Solvability

Luca Paolini, Simona Ronchi Della Rocca (2010)

RAIRO - Theoretical Informatics and Applications

The notion of solvability in the call-by-value λ-calculus is defined and completely characterized, both from an operational and a logical point of view. The operational characterization is given through a reduction machine, performing the classical β-reduction, according to an innermost strategy. In fact, it turns out that the call-by-value reduction rule is too weak for capturing the solvability property of terms. The logical characterization is given through an intersection type assignment system,...

Can interestingness measures be usefully visualized?

Robert Susmaga, Izabela Szczech (2015)

International Journal of Applied Mathematics and Computer Science

The paper presents visualization techniques for interestingness measures. The process of measure visualization provides useful insights into different domain areas of the visualized measures and thus effectively assists their comprehension and selection for different knowledge discovery tasks. Assuming a common domain form of the visualized measures, a set of contingency tables, which consists of all possible tables having the same total number of observations, is constructed. These originally four-dimensional...

Capital budgeting problems with fuzzy cash flows.

Christer Carlsson, Robert Fuller (1999)

Mathware and Soft Computing

We consider the internal rate of return (IRR) decision rule in capital budgeting problems with fuzzy cash flows. The possibility distribution of the IRR at any r ≥ 0, is defined to be the degree of possibility that the (fuzzy) net present value of the project with discount factor r equals to zero. Generalizing our earlier results on fuzzy capital budegeting problems [Car99] we show that the possibility distribution of the {IRR} is a highly nonlinear function which is getting more and more unbalanced...

Cascading classifiers

Ethem Alpaydin, Cenk Kaynak (1998)

Kybernetika

We propose a multistage recognition method built as a cascade of a linear parametric model and a k -nearest neighbor ( k -NN) nonparametric classifier. The linear model learns a “rule” and the k -NN learns the “exceptions” rejected by the “rule.” Because the rule-learner handles a large percentage of the examples using a simple and general rule, only a small subset of the training set is stored as exceptions during training. Similarly during testing, most patterns are handled by the rule -learner and...

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