No description available.

Machine translation (MT) is experiencing a renaissance. On one hand, machine translation is becoming more common and used in ever larger scale, on the other hand many translators have an almost hostile attitude towards machine translation programs and those translators who use MT as a tool. Either it is assumed that the MT can never be as good as a human translation or machine translation is viewed as the ultimate enemy of the translator and as a job killer. The article discusses with various examples the limits and possibilities of machine translation. It demonstrates that machine translation can be better than human translations – even if they were made by experienced professional translators. The paper also reports the results of a test that showed that translation customers must expect that even well-known and expensive translation service providers deliver a quality that is on par with poor MT. Overall, it is argued that machine translation programs are no more and no less than an additional tool with which the translation industry can satisfy certain requirements. This abstract was also – as the entire article – automatically translated into English.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18965号 / 工博第4007号 / 新制||工||1617(附属図書館) / 31916 / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 藤井 聡, 准教授 SCHMOECKER,JAN-DirK, 准教授 神田 佑亮 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

No description available.

A novel approach to the gain estimation problem,using a multi-armed bandit formulation, is studied. The gain estimation problem deals with the problem of estimating the largest L2-gain that signal of bounded norm experiences when passing through a linear and time-invariant system. Under certain conditions, this new approach is guaranteed to surpass traditional System Identification methods in terms of accuracy.The bandit algorithms Upper Confidence Bound, Thompson Sampling and Weighted Thompson Sampling are implemented with the aim of designing the optimal input for maximizing the gain of an unknown system. The regret performance of each algorithm is studied using simulations on a test system. Upper Confidence Bound, with exploration parameter set to zero, performed the best among all tested values for this parameter. Weighted Thompson Sampling performed better than Thompson Sampling.

No description available.

Machine translation (MT) is experiencing a renaissance. On one hand, machine translation is becoming more common and used in ever larger scale, on the other hand many translators have an almost hostile attitude towards machine translation programs and those translators who use MT as a tool. Either it is assumed that the MT can never be as good as a human translation or machine translation is viewed as the ultimate enemy of the translator and as a job killer. The article discusses with various examples the limits and possibilities of machine translation. It demonstrates that machine translation can be better than human translations – even if they were made by experienced professional translators. The paper also reports the results of a test that showed that translation customers must expect that even well-known and expensive translation service providers deliver a quality that is on par with poor MT. Overall, it is argued that machine translation programs are no more and no less than an additional tool with which the translation industry can satisfy certain requirements. This abstract was also – as the entire article – automatically translated into English.

No description available.

No description available.

No description available.