Ecotechnological relations between aquatic-microbes & turbidity with machine learning techniques

Biogeography of aquatic microbes

Aquatic microbes, like all organisms, have biogeographies, but this subject has attracted relatively little attention. In this review, recent results exploiting techniques of molecular biology are summarized to place in perspective the studies of this Theme Section. The studies considered concern large-scale patterns of spatial distribution among heterotrophic planktonic prokaryotic and eukaryo...

Challenges to Machine Learning: Relations Between Reality and Appearance

Machine learning research, e.g. as described in [Mit97], has had its goal the discovery of relations among observations, i.e. appearances. This is inadequate for science, because there is a reality behind appearance, e.g. material objects are built up from atoms. Atoms are just as real as dogs, only harder to observe, and the atomic theory arose long before there was any idea of how big atoms w...

Machine Learning of Temporal Relations

This paper investigates a machine learning approach for temporally ordering and anchoring events in natural language texts. To address data sparseness, we used temporal reasoning as an oversampling method to dramatically expand the amount of training data, resulting in predictive accuracy on link labeling as high as 93% using a Maximum Entropy classifier on human annotated data. This method com...

Learning of Semantic Relations between Ontology Concepts using Statistical Techniques

Acquiring domain knowledge for constructing ontologies is a resource demanding and time consuming task. Thus, the automatic or semi-automatic construction, enrichment and adaptation of ontologies, the so-called ontology learning task is highly desired. Although an emerging field, a significant amount of research has been performed in ontology learning, leading to a large number of proposed appr...

Machine Learning Techniques