The Experts below are selected from a list of 2385 Experts worldwide ranked by ideXlab platform
Beata Zielosko - One of the best experts on this subject based on the ideXlab platform.
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dynamic programming approach to optimization of approximate decision rules
Information Sciences, 2013Co-Authors: Talha Amin, Mikhail Moshkov, Igor Chikalov, Beata ZieloskoAbstract:This paper is devoted to the study of an extension of dynamic programming approach which allows sequential optimization of approximate decision rules relative to the length and coverage. We introduce an uncertainty measure R(T) which is the Number of unordered pairs of rows with different decisions in the decision table T. For a Nonnegative Real Number @b, we consider @b-decision rules that localize rows in subtables of T with uncertainty at most @b. Our algorithm constructs a directed acyclic graph @D"@b(T) which nodes are subtables of the decision table T given by systems of equations of the kind ''attribute=value''. This algorithm finishes the partitioning of a subtable when its uncertainty is at most @b. The graph @D"@b(T) allows us to describe the whole set of so-called irredundant @b-decision rules. We can describe all irredundant @b-decision rules with minimum length, and after that among these rules describe all rules with maximum coverage. We can also change the order of optimization. The consideration of irredundant rules only does not change the results of optimization. This paper contains also results of experiments with decision tables from UCI Machine Learning Repository.
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dynamic programming approach for partial decision rule optimization
Fundamenta Informaticae, 2012Co-Authors: Talha Amin, Mikhail Moshkov, Igor Chikalov, Beata ZieloskoAbstract:This paper is devoted to the study of an extension of dynamic programming approach which allows optimization of partial decision rules relative to the length or coverage. We introduce an uncertainty measure J(T) which is the difference between Number of rows in a decision table T and Number of rows with the most common decision for T. For a Nonnegative Real Number γ, we consider γ-decision rules (partial decision rules) that localize rows in subtables of T with uncertainty at most γ. Presented algorithm constructs a directed acyclic graph Δγ(T) which nodes are subtables of the decision table T given by systems of equations of the kind “attribute = value”. This algorithm finishes the partitioning of a subtable when its uncertainty is at most γ. The graph Δγ(T) allows us to describe the whole set of so-called irredundant γ-decision rules. We can optimize such set of rules according to length or coverage. This paper contains also results of experiments with decision tables from UCI Machine Learning Repository.
Whiteland, Markus A. - One of the best experts on this subject based on the ideXlab platform.
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All Growth Rates of Abelian Exponents Are Attained by Infinite Binary Words
LIPIcs - Leibniz International Proceedings in Informatics. 45th International Symposium on Mathematical Foundations of Computer Science (MFCS 2020), 2020Co-Authors: Whiteland, Markus A.Abstract:We consider repetitions in infinite words by making a novel inquiry to the maximum eventual growth rate of the exponents of abelian powers occurring in an infinite word. Given an increasing, unbounded function f: ? ? ?, we construct an infinite binary word whose abelian exponents have limit superior growth rate f. As a consequence, we obtain that every Nonnegative Real Number is the critical abelian exponent of some infinite binary word
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Every Nonnegative Real Number is an abelian critical exponent
'Springer Science and Business Media LLC', 2019Co-Authors: Peltomäki Jarkko, Whiteland, Markus A.Abstract:The abelian critical exponent of an infinite word $w$ is defined as the maximum ratio between the exponent and the period of an abelian power occurring in $w$. It was shown by Fici et al. that the set of finite abelian critical exponents of Sturmian words coincides with the Lagrange spectrum. This spectrum contains every large enough positive Real Number. We construct words whose abelian critical exponents fill the remaining gaps, that is, we prove that for each Nonnegative Real Number $\theta$ there exists an infinite word having abelian critical exponent $\theta$. We also extend this result to the $k$-abelian setting.Comment: 11 page
Talha Amin - One of the best experts on this subject based on the ideXlab platform.
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dynamic programming approach to optimization of approximate decision rules
Information Sciences, 2013Co-Authors: Talha Amin, Mikhail Moshkov, Igor Chikalov, Beata ZieloskoAbstract:This paper is devoted to the study of an extension of dynamic programming approach which allows sequential optimization of approximate decision rules relative to the length and coverage. We introduce an uncertainty measure R(T) which is the Number of unordered pairs of rows with different decisions in the decision table T. For a Nonnegative Real Number @b, we consider @b-decision rules that localize rows in subtables of T with uncertainty at most @b. Our algorithm constructs a directed acyclic graph @D"@b(T) which nodes are subtables of the decision table T given by systems of equations of the kind ''attribute=value''. This algorithm finishes the partitioning of a subtable when its uncertainty is at most @b. The graph @D"@b(T) allows us to describe the whole set of so-called irredundant @b-decision rules. We can describe all irredundant @b-decision rules with minimum length, and after that among these rules describe all rules with maximum coverage. We can also change the order of optimization. The consideration of irredundant rules only does not change the results of optimization. This paper contains also results of experiments with decision tables from UCI Machine Learning Repository.
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dynamic programming approach for partial decision rule optimization
Fundamenta Informaticae, 2012Co-Authors: Talha Amin, Mikhail Moshkov, Igor Chikalov, Beata ZieloskoAbstract:This paper is devoted to the study of an extension of dynamic programming approach which allows optimization of partial decision rules relative to the length or coverage. We introduce an uncertainty measure J(T) which is the difference between Number of rows in a decision table T and Number of rows with the most common decision for T. For a Nonnegative Real Number γ, we consider γ-decision rules (partial decision rules) that localize rows in subtables of T with uncertainty at most γ. Presented algorithm constructs a directed acyclic graph Δγ(T) which nodes are subtables of the decision table T given by systems of equations of the kind “attribute = value”. This algorithm finishes the partitioning of a subtable when its uncertainty is at most γ. The graph Δγ(T) allows us to describe the whole set of so-called irredundant γ-decision rules. We can optimize such set of rules according to length or coverage. This paper contains also results of experiments with decision tables from UCI Machine Learning Repository.
Markus A. Whiteland - One of the best experts on this subject based on the ideXlab platform.
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Every Nonnegative Real Number is an abelian critical exponent
arXiv: Formal Languages and Automata Theory, 2019Co-Authors: Jarkko Peltomäki, Markus A. WhitelandAbstract:The abelian critical exponent of an infinite word $w$ is defined as the maximum ratio between the exponent and the period of an abelian power occurring in $w$. It was shown by Fici et al. that the set of finite abelian critical exponents of Sturmian words coincides with the Lagrange spectrum. This spectrum contains every large enough positive Real Number. We construct words whose abelian critical exponents fill the remaining gaps, that is, we prove that for each Nonnegative Real Number $\theta$ there exists an infinite word having abelian critical exponent $\theta$. We also extend this result to the $k$-abelian setting.
Igor Chikalov - One of the best experts on this subject based on the ideXlab platform.
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dynamic programming approach to optimization of approximate decision rules
Information Sciences, 2013Co-Authors: Talha Amin, Mikhail Moshkov, Igor Chikalov, Beata ZieloskoAbstract:This paper is devoted to the study of an extension of dynamic programming approach which allows sequential optimization of approximate decision rules relative to the length and coverage. We introduce an uncertainty measure R(T) which is the Number of unordered pairs of rows with different decisions in the decision table T. For a Nonnegative Real Number @b, we consider @b-decision rules that localize rows in subtables of T with uncertainty at most @b. Our algorithm constructs a directed acyclic graph @D"@b(T) which nodes are subtables of the decision table T given by systems of equations of the kind ''attribute=value''. This algorithm finishes the partitioning of a subtable when its uncertainty is at most @b. The graph @D"@b(T) allows us to describe the whole set of so-called irredundant @b-decision rules. We can describe all irredundant @b-decision rules with minimum length, and after that among these rules describe all rules with maximum coverage. We can also change the order of optimization. The consideration of irredundant rules only does not change the results of optimization. This paper contains also results of experiments with decision tables from UCI Machine Learning Repository.
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dynamic programming approach for partial decision rule optimization
Fundamenta Informaticae, 2012Co-Authors: Talha Amin, Mikhail Moshkov, Igor Chikalov, Beata ZieloskoAbstract:This paper is devoted to the study of an extension of dynamic programming approach which allows optimization of partial decision rules relative to the length or coverage. We introduce an uncertainty measure J(T) which is the difference between Number of rows in a decision table T and Number of rows with the most common decision for T. For a Nonnegative Real Number γ, we consider γ-decision rules (partial decision rules) that localize rows in subtables of T with uncertainty at most γ. Presented algorithm constructs a directed acyclic graph Δγ(T) which nodes are subtables of the decision table T given by systems of equations of the kind “attribute = value”. This algorithm finishes the partitioning of a subtable when its uncertainty is at most γ. The graph Δγ(T) allows us to describe the whole set of so-called irredundant γ-decision rules. We can optimize such set of rules according to length or coverage. This paper contains also results of experiments with decision tables from UCI Machine Learning Repository.
