Michael Geilke

Michael Geilke

PhD

About

8
Publications
504
Reads
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32
Citations
Citations since 2016
3 Research Items
21 Citations
201620172018201920202021202201234567
201620172018201920202021202201234567
201620172018201920202021202201234567
201620172018201920202021202201234567

Publications

Publications (8)
Article
Full-text available
We address the problem of estimating discrete, continuous, and conditional joint densities online, i.e., the algorithm is only provided the current example and its current estimate for its update. The family of proposed online density estimators, estimation of densities online (EDO), uses classifier chains to model dependencies among features, wher...
Conference Paper
Full-text available
The joint density of a data stream is suitable for performing data mining tasks without having access to the original data. However, the methods proposed so far only target a small to medium number of variables, since their estimates rely on representing all the interdependencies between the variables of the data. High-dimensional data streams, whi...
Conference Paper
Full-text available
Discovering changes in the data distribution of streams and discovering recurrent data distributions are challenging problems in data mining and machine learning. Both have received a lot of attention in the context of classification. With the ever increasing growth of data, however, there is a high demand of compact and universal representations o...
Conference Paper
Full-text available
Data mining and machine learning algorithms usually operate directly on the data. However, if the data is not available at once or consists of billions of instances, these algorithms easily become infeasible with respect to memory and run-time concerns. As a solution to this problem, we propose a framework, called MiDEO (Mining Density Estimates in...
Conference Paper
Full-text available
We address the problem of estimating a discrete joint density online, that is, the algorithm is only provided the current example and its current estimate. The proposed online estimator of discrete densities, EDDO (Estimation of Discrete Densities Online), uses classifier chains to model dependencies among features. Each classifier in the chain est...
Conference Paper
This article proposes polynomial-time algorithms for learning typed pattern languages—formal languages that are generated by patterns consisting of terminal symbols and typed variables. A string is generated by a typed pattern by substituting all variables with strings of terminal symbols that belong to the corresponding types. The algorithms prese...
Conference Paper
Patterns provide a simple, yet powerful means of describing formal languages. However, for many applications, neither patterns nor their generalized versions of typed patterns are expressive enough. This paper extends the model of (typed) patterns by allowing relations between the variables in a pattern. The resulting formal languages are called Re...

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