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Martin Janzura

dblp:21/2467 · DBLP profile ↗
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4ranked-venue papers
4as first author
0since 2021 · last 2006
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 2 first-authorDatabases, data management, data science and information retrieval · 2 · 2 first-authorTheory of computation · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Information theory · 75% Mathematical optimization · 25%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Mathematical optimization › numerical analysis
error estimation		 0.011996
Minimum entropy of error estimation for discrete random variables · IEEE Trans. Inf. Theory 1996
Information theory
hypothesis testing		 0.011996
Minimum entropy of error estimation for discrete random variables · IEEE Trans. Inf. Theory 1996
Information theory › information measures › entropy › generalized entropy
minimum entropy		 0.011996
Minimum entropy of error estimation for discrete random variables · IEEE Trans. Inf. Theory 1996
Information theory › hypothesis testing › binary hypothesis testing
neyman-pearson detection		 0.011996
Minimum entropy of error estimation for discrete random variables · IEEE Trans. Inf. Theory 1996
YearPublicationVenuePosition
2006 A simulated annealing-based method for learning Bayesian networks from statistical data
abstract
The problem of learning Bayesian networks from statistical data is described and reformulated as a discrete optimization problem. For a solution we employ the stochastic algorithm that is known as simulated annealing and that is based on the Markov Chain Monte Carlo approach. Numerical examples are included to illustrate the efficiency of the method. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 335–348, 2006.
Martin Janzura, Jan Nielsen
 Int. J. Intell. Syst.1
2003 On the exponential representation problem
Martin Janzura
 Soft Comput.1
1996 Minimum entropy of error estimation for discrete random variables
abstract
The principle of minimum entropy of error estimation (MEEE) is formulated for discrete random variables. In the case when the random variable to be estimated is binary, we show that the MEEE is given by a Neyman-Pearson-type strictly monotonous test. In addition, the asymptotic behavior of the error probabilities is proved to be equivalent to that of the Bayesian test.
Martin Janzura, Timo Koski, Antonín Otáhal
 IEEE Trans. Inf. Theory1
1994 Minimum Entropy of Error Principle in Estimation
Martin Janzura, Timo Koski, Antonín Otáhal
 Inf. Sci.1