Segmentierung mit Gaborfiltern zur Induktion struktureller Klassifikatoren auf Bilddaten. Technical Report (Master thesis), Herbrich, Ralf PhD thesis, Technical University Berlin. (1997).
Generation of Task-Specific Segmentation Procedures as a Model Selection Task. Herbrich, Ralf; Scheffer, Tobias (1997). 11–21.
In image segmentation problems, there is usually a vast amount of filter operations available, a subset of which has to be selected and instantiated in order to obtain a satisfactory segmentation procedure for a particular do- main. In supervised segmentation, a mapping from features, such as filter outputs for individual pixels, to classes is induced automatically. However, since the sample size required for supervised learning grows exponentially in the number of features it is not feasible to learn a segmentation procedure from a large amount of possible filters. But we argue that automatic model selection methods are able to select a region model in terms of some filters.
Unbiased Assesment of Learning Algorithms. Scheffer, Tobias; Herbrich, Ralf (1997). 798–803.
In order to rank the performance of machine learning algorithms, many researchs conduct experiments on benchmark datasets. Since most learning algorithms have domain-specific parameters, it is a popular custom to adapt these parameters to obtain a minimal error rate on the test set. The same rate is used to rank the algorithm which causes an optimistic bias. We quantify this bias, showing in particular that an algorithm with more parameters will probably be ranked higher than an equally good algorithm with fewer parameters. We demonstrate this result, showing the number of parameters and trials required in order to pretend to outperform C4.5 or FOIL, respectively, for various benchmark problems. We then describe how unbiased ranking experiments should be conducted.
