On the Teachability of Randomized Learners

The present paper introduces a new model for teaching {em randomized learners}. Our new model, though based on the classical teaching dimension model, allows to study the influence of various parameters such as the learner\u27s memory size, its ability to provide or to not provide feedback, and the influence of the order in which examples are presented. Furthermore, within the new model it is possible to investigate new aspects of teaching like teaching from positive data only or teaching with inconsistent teachers. Furthermore, we provide characterization theorems for teachability from positive data for both ordinary teachers and inconsistent teachers with and without feedback

Normalized Information Distance

The normalized information distance is a universal distance measure for objects of all kinds. It is based on Kolmogorov complexity and thus uncomputable, but there are ways to utilize it. First, compression algorithms can be used to approximate the Kolmogorov complexity if the objects have a string representation. Second, for names and abstract concepts, page count statistics from the World Wide Web can be used. These practical realizations of the normalized information distance can then be applied to machine learning tasks, expecially clustering, to perform feature-free and parameter-free data mining. This chapter discusses the theoretical foundations of the normalized information distance and both practical realizations. It presents numerous examples of successful real-world applications based on these distance measures, ranging from bioinformatics to music clustering to machine translation.Comment: 33 pages, 12 figures, pdf, in: Normalized information distance, in: Information Theory and Statistical Learning, Eds. M. Dehmer, F. Emmert-Streib, Springer-Verlag, New-York, To appea

Biosynthesis of Salmonella enterica [NiFe]-hydrogenase-5 : probing the roles of system-specific accessory proteins

A subset of bacterial [NiFe]-hydrogenases have been shown to be capable of activating dihydrogen-catalysis under aerobic conditions; however, it remains relatively unclear how the assembly and activation of these enzymes is carried out in the presence of air. Acquiring this knowledge is important if a generic method for achieving production of O2-resistant [NiFe]-hydrogenases within heterologous hosts is to be developed. Salmonella enterica serovar Typhimurium synthesizes the [NiFe]-hydrogenase-5 (Hyd-5) enzyme under aerobic conditions. As well as structural genes, the Hyd-5 operon also contains several accessory genes that are predicted to be involved in different stages of biosynthesis of the enzyme. In this work, deletions in the hydF, hydG, and hydH genes have been constructed. The hydF gene encodes a protein related to Ralstonia eutropha HoxO, which is known to interact with the small subunit of a [NiFe]-hydrogenase. HydG is predicted to be a fusion of the R. eutropha HoxQ and HoxR proteins, both of which have been implicated in the biosynthesis of an O2-tolerant hydrogenase, and HydH is a homologue of R. eutropha HoxV, which is a scaffold for [NiFe] cofactor assembly. It is shown here that HydG and HydH play essential roles in Hyd-5 biosynthesis. Hyd-5 can be isolated and characterized from a ΔhydF strain, indicating that HydF may not play the same vital role as the orthologous HoxO. This study, therefore, emphasises differences that can be observed when comparing the function of hydrogenase maturases in different biological systems

Teaching randomized learners with feedback

The present paper introduces a new model for teaching randomized learners. Our new model, though based on the classical teaching dimension model, allows to study the influence of the learner's memory size and of the presence or absence of feedback. Moreover, in the new model the order in which examples are presented may influence the teaching process. The resulting models are related to Markov decision processes, and characterizations of optimal teachers for memoryless learners with feedback and for learners with infinite memory and feedback are shown. Furthermore, in the new model it is possible to investigate new aspects of teaching like teaching from positive data only or teaching with inconsistent teachers. Characterization theorems for teachability from positive data for both ordinary teachers and inconsistent teachers with and without feedback are provided