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Leveraging the Semantics of Topics Maps ; 2nd International Conference on Topic Maps Research and Applications, TMRA 2006, Leipzig, Germany, October 11-12, 2006, Revised Selected papers
The papers in this volume were presented at TMRA 2006, the International Conference on Topic Maps Research and Applications, held October 11–12, 2006, in Leipzig, Germany. TMRA 2006 was the second conference of an annual series of international conferences dedicated to Topic Maps in research and industry.
Charting the Topic Maps Research and Applications Landscape ; 1st International Workshop on Topic Map Research and Applications, TMRA 2005, Leipzig, Germany, October 6-7, 2005, Revised Selected Papers
The papers in this volume were presented at the workshop “Topic Map Research and Applications 2005” held on October 6-7, 2005, in Leipzig. TMRA 2005 was the first workshop of an annual series of international workshops dedicated to topic maps in research and industry. As the motto “Charting the Topic Maps Research and Applications Landscape” suggests, the aim of TMRA 2005 was to identify the primary open issues in research, learn about who is working on what, bring together researchers and application pioneers, stimulate the systematic tackling of such issues, and foster the exchange of ideas in a stimulating setting.
Automated machine learning : Methods, systems, challenges
This book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself.
