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Search Algorithms in C Language
Nejezchleb, Ivan ; Lukáš, Roman (referee) ; Honzík, Jan M. (advisor)
Searching in all possible forms is at the present time widely used operation not only in the subject of information technology. So the understanding and the grasp of the basic searching algorithms is necessary for everyone who wants to develop services containing searching mechanism. In my work I deal with the searching from the view of C language programmer. I will introduce basic searching algorithms and demo applications of their principles. Goal of whole work is to create study aid for easier understanding of the search subject.
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Search Algorithms in C Language
Nejezchleb, Ivan ; Lukáš, Roman (referee) ; Honzík, Jan M. (advisor)
Searching in all possible forms is at the present time widely used operation not only in the subject of information technology. So the understanding and the grasp of the basic searching algorithms is necessary for everyone who wants to develop services containing searching mechanism. In my work I deal with the searching from the view of C language programmer. I will introduce basic searching algorithms and demo applications of their principles. Goal of whole work is to create study aid for easier understanding of the search subject.
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Sequential Retreating Search Methods in Feature Selection
Somol, Petr ; Pudil, Pavel
Inspired by Floating Search, our new pair of methods, the Sequential Forward Retreating Search (SFRS) and Sequential Backward Retreating Search (SBRS) is exceptionally suitable for Wrapper based feature selection. (Conversely, it cannot be used with monotonic criteria.) Unlike most of other known sub-optimal search methods, both the SFRS and SBRS are parameter-free deterministic sequential procedures that incorporate in the optimization process both the search for the best subset and the determination of the best subset size. The subset yielded by either of the two new methods is to be expected closer to optimum than the best of all subsets yielded in one run of the Floating Search. Retreating Search time complexity is to be expected slightly worse but in the same order of magnitude as that of the Floating Search. In addition to introducing the new methods we provide a testing framework to evaluate them with respect to other existing tools.
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Vyhodnocení stability jednotlivých metod i skupin metod výběru příznaků, který optimalizují kardinalitu podmnožiny příznaků
Somol, Petr ; Novovičová, Jana
Stability (robustness) of feature selection methods is a topic of recent interest yet often neglected importance with direct impact on the reliability of machine learning systems. We investigate the problem of evaluating the stability of feature selection processes yielding subsets of varying size. We introduce several novel feature selection stability measures and adjust some existing measures in a unifying framework that offers broad insight into the stability problem. We study in detail the properties of considered measures and demonstrate on various examples what information about the feature selection process can be gained. We also introduce an alternative approach to feature selection evaluation in form of measures that enable comparing the similarity of two feature selection processes. These measures enable comparing, e.g., the output of two feature selection methods or two runs of one method with different parameters. The information obtained using the considered stability and similarity measures is shown usable for assessing feature selection methods (or criteria) as such
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