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s and updates received by IAIA online per submission and updating guidelines and with the presenting author registered in full on or before 22 March 2005 are included. Abstracts (as available) are arranged in the order in which presentations are listed in the program. An author index is included at the end of this volume. Abstracts have been formatted for style consistency; text and contact inf...
In this paper we introduce a k-anonymous vector space model, which can be used as an index of a set of confidential documents. This model allows to index, for example, encrypted data. New documents can be added or removed while maintaining the k-anonymity property of the vector space. URL http://dx.doi.org/10.1007/978-3-642-54568-9_23 [10] Source URL: https://www.iiia.csic.es/en/node/54461 Link...
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it is a great pleasure to receive two valuable comments about our article regarding sudden cardiac death (scd) in young athletes from two well known experts working in the field of preparticipation evaluation and prevention of sports injuries. we, as authors, generally agree with both authors' comments. however, some considerations should be re-emphasized here. .....
In this paper, the author first analyzed the model and structure of the native XML database (NXD) and studied the application schema and structure of the GML 3.x implementation. Based on these, the author proposed the schema cluster storage and the numerical index technology to store and index GML spatial data, and adopted JavaBean technology to extent the inner module of the eXist-db to prove ...
Author Index 96 List of Participants 102
Author Index LB92–LB101 Abstract Author Disclosure Information LB102–LB117 June 2016 | Volume 65 | Suppl. 1A | www.diabetes.org/diabetesAuthor Disclosure Information LB102–LB117 June 2016 | Volume 65 | Suppl. 1A | www.diabetes.org/diabetes
We present an overview of learning theory including its statistical and computational aspects. We start by giving a probabilistic formulation of learning problems including classification and regression where the learner knows nothing about the probability distribution generating the data. We then consider the principle of empirical risk minimization (ERM) that chooses a function from a given c...
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