Summary We discuss the validation of machine learning models, which is standard practice in determining model efficacy and generalizability. We argue that internal validation approaches, such as cross-validation and bootstrap, cannot guarantee the quality of a machine learning model due to potentially biased training data and the complexity of the validation procedure itself. For better evaluating the generalization ability of a learned model, we suggest leveraging on external data sources from elsewhere as validation datasets, namely external validation. Due to the lack of research attractions on external validation, especially a well-structured and comprehensive study, we discuss the necessity for external validation and propose two extensions of the external validation approach that may help reveal the true domain-relevant model from a candidate set. Moreover, we also suggest a procedure to check whether a set of validation datasets is valid and introduce statistical reference points for detecting external data problems.

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Walk (CSAW) as a model of protein folding. Dr Pruessner gave an overview on the current development of Self-Organized Criticality (SOC). The special talks were followed by talks from researchers from Australia, Japan, the Netherlands, Indonesia, France, Russia, Brunei, Turkey and Singapore. The topics presented were wide-ranging, spanning from quantum entanglement to the complexity theory of business, social and biophysical systems.