Paul Komarek

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Binary classification is a core data mining task. For large datasets or real-time applications, desirable classi-fiers are accurate, fast, and need no parameter tuning. We present a simple implementation of logistic regression that meets these requirements. A combination of regulariza-tion, truncated Newton methods, and iteratively re-weighted least squares(More)
Although popular and extremely well established in mainstream statistical data analysis, logistic regression is strangely absent in the field of data mining. There are two possible explanations of this phenomenon. First, there might be an assumption that any tool which can only produce linear classification boundaries is likely to be trumped by more modern(More)
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