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Self-adaptation of parameters in a learning classifier system ensemble machine

Maciej TroćOlgierd Unold — 2010

International Journal of Applied Mathematics and Computer Science

Self-adaptation is a key feature of evolutionary algorithms (EAs). Although EAs have been used successfully to solve a wide variety of problems, the performance of this technique depends heavily on the selection of the EA parameters. Moreover, the process of setting such parameters is considered a time-consuming task. Several research works have tried to deal with this problem; however, the construction of algorithms letting the parameters adapt themselves to the problem is a critical and open problem...

Real-valued GCS classifier system

Łukasz CieleckiOlgierd Unold — 2007

International Journal of Applied Mathematics and Computer Science

Learning Classifier Systems (LCSs) have gained increasing interest in the genetic and evolutionary computation literature. Many real-world problems are not conveniently expressed using the ternary representation typically used by LCSs and for such problems an interval-based representation is preferable. A new model of LCSs is introduced to classify real-valued data. The approach applies the continous-valued context-free grammar-based system GCS. In order to handle data effectively, the terminal...

Projection-based text line segmentation with a variable threshold

Roman PtakBartosz ZygadłoOlgierd Unold — 2017

International Journal of Applied Mathematics and Computer Science

Document image segmentation into text lines is one of the stages in unconstrained handwritten document recognition. This paper presents a new algorithm for text line separation in handwriting. The developed algorithm is based on a method using the projection profile. It employs thresholding, but the threshold value is variable. This permits determination of low or overlapping peaks of the graph. The proposed technique is shown to improve the recognition rate relative to traditional methods. The...

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