rformance can be better if changes to the database are batched: turn off autocommit; add multiple SQL statements using the Statement.addBatch() method; execute Statement.executeBatch().
<property> <name>http.agent.name</name> <value></value> <description>HTTP 'User-Agent' request header. MUST NOT be empty - please set this to a single word uniquely related to your organization. NOTE: You should also check other related properties: http.robots.agents http.agent.description http.agent.url http.agent.email http.agent.version and set their values appropriately. </description> </property> <property> <name>http.agent.description</name> <value></value> <description>Further description of our bot- this text is used in the User-Agent header. It appears in parenthesis after the agent name. </description> </property> <property> <name>http.agent.url</name> <value></value> <description>A URL to advertise in the User-Agent header. This will appear in parenthesis after the agent name. Custom dictates that this should be a URL of a page explaining the purpose and behavior of this crawler. </description> </property> <property> <name>http.agent.email</name> <value></value> <description>An email address to advertise in the HTTP 'From' request header and User-Agent header. A good practice is to mangle this address (e.g. 'info at example dot com') to avoid spamming. </description> </property>
mendation service which can be called via HTTP by BibSonomy's recommender when a user posts a bookmark or publication. All participating recommenders are called on each posting process, one of them is choosen to actually deliver the results to the user. We can then measure
This school is suitable for all levels, both for people without previous knowledge in Machine Learning, and those wishing to broaden their expertise in this area. It will allow the participants to get in touch with international experts in this field. Exchange of students, joint publications and joint projects will result because of this collaboration. For research students, the summer school provides a unique, high-quality, and intensive period of study. It is ideally suited for students currently pursuing, or intending to pursue, research in Machine Learning or related fields. For IT professionals who use Machine Learning will find that the summer school provides relevant knowledge and exposure to contemporary techniques. In addition, they will benefit by direct interaction with top-notch researchers and knowledge workers. Previous experience indicates that personnel from both the industry as well as national laboratories like CSIRO, DSTO benefit immensely from the school. For academics, the summer school is an excellent opportunity to help getting started in research on novel topics in Machine Learning. It provides an ideal forum for networking and discussions. Academics will also benefit from interaction with IT professionals which will lead to a deeper understanding of real life problems. Organizers, this summer school is organized by the Computer Sciences Laboratory of the Australian National University (CSL@ANU) and the Statistical Machine Learning program of the National ICT Australia (SML@NICTA), jointly with support from the Max-Planck-Institute for Biological Cybernetics in Tübingen and the Pascal Netwok. Please visit www.mlss.cc for more information about the previous summer schools. Local organizers are Li Cheng, Marcus Hutter, and Alex Smola.
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