图 书 馆开课单位--东京大学
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11Controlling Humanoid Robots by Means of Genetic Programming[基于遗传规划的仿人机器人控制]
Hitoshi Iba(东京大学) We show the real-world applications of EC (evolutionary computation) to robotics, which is called "evolutionary robotics". Machine Learning ...
热度：37
Hitoshi Iba(东京大学) We show the real-world applications of EC (evolutionary computation) to robotics, which is called "evolutionary robotics". Machine Learning ...
热度：37
12Measuring the Similarity between Implicit Semantic Relations from the Web[从Web中测量隐含语义关系的相似性]
Danushka Bollegala;Yutaka Matsuo;Mitsuru Ishizuka(东京大学) Measuring the similarity between semantic relations that hold among entities is an important and necessary step in various Web related tasks such as r...
热度：60
Danushka Bollegala;Yutaka Matsuo;Mitsuru Ishizuka(东京大学) Measuring the similarity between semantic relations that hold among entities is an important and necessary step in various Web related tasks such as r...
热度：60
13Baryon Asymmetry of the Universe[宇宙的重子不对称性]
Hitoshi Murayama(东京大学) In two lectures, the following topics will be discussed: (1) Why baryon asymmetry is a problem at all (2) Review of the Sakharov's conditions (3) Why...
热度：48
Hitoshi Murayama(东京大学) In two lectures, the following topics will be discussed: (1) Why baryon asymmetry is a problem at all (2) Review of the Sakharov's conditions (3) Why...
热度：48
14Eigenmode of Decision-by-Majority Process on Complex Networks[复杂网络上多数决策过程的特征模式]
Makoto Uchida(东京大学) The nature of opinion formation dynamics in complex networks is investigated using eigenmode analysis. Opinion formation dynamics is modeled by a deci...
热度：105
Makoto Uchida(东京大学) The nature of opinion formation dynamics in complex networks is investigated using eigenmode analysis. Opinion formation dynamics is modeled by a deci...
热度：105
15Collusion-Resistant Privacy-Preserving Data Mining[防止串通的隐私保护数据挖掘]
Bin Yang(东京大学) Recent research in privacy-preserving data mining (PPDM) has become increasingly popular due to the wide application of data mining and the increased ...
热度：61
Bin Yang(东京大学) Recent research in privacy-preserving data mining (PPDM) has become increasingly popular due to the wide application of data mining and the increased ...
热度：61
16Knowledge Discovery of Multiple-topic Document using Parametric Mixture Model with Dirichlet Prior[基于Dirichlet先验参数混合模型的多主题文献知识发现]
Issei Sato(东京大学) Documents, such as those seen onWikipedia and Folksonomy, have tended to be assigned with multiple topics as a meta-data. Therefore, it is more and mo...
热度：35
Issei Sato(东京大学) Documents, such as those seen onWikipedia and Folksonomy, have tended to be assigned with multiple topics as a meta-data. Therefore, it is more and mo...
热度：35
17Proto-Predictive Representation of States with Simple Recurrent Temporal-Difference Networks[具有简单递归时差网络的状态的原始预测表示]
Takaki Makino(东京大学) We propose a new neural network architecture, called Simple Recurrent Temporal-Difference Networks (SR-TDNs), that learns to predict future observatio...
热度：37
Takaki Makino(东京大学) We propose a new neural network architecture, called Simple Recurrent Temporal-Difference Networks (SR-TDNs), that learns to predict future observatio...
热度：37
18On-line Discovery of Temporal-Difference Networks[时间差网络的在线发现]
Takaki Makino(东京大学) We present an algorithm for on-line, incremental discovery of temporal-difference (TD) networks. The key contribution is the establishment of three cr...
热度：55
Takaki Makino(东京大学) We present an algorithm for on-line, incremental discovery of temporal-difference (TD) networks. The key contribution is the establishment of three cr...
热度：55
19Frequency-aware Truncated methods for Sparse Online Learning[用于稀疏在线学习的频率感知截断方法]
Hidekazu Oiwa(东京大学) Online supervised learning with L1-regularization has gained attention recently because it generally requires less computational time and a smaller sp...
热度：65
Hidekazu Oiwa(东京大学) Online supervised learning with L1-regularization has gained attention recently because it generally requires less computational time and a smaller sp...
热度：65
20Mining Peculiar Compositions of Frequent Substrings from Sparse Text Data Using Background Texts[利用背景文本从稀疏文本数据中挖掘频繁字符串的特殊成分]
Daisuke Ikeda(东京大学) We consider mining unusual patterns from text T. Unlike existing methods which assume probabilistic models and use simple estimation methods, we emplo...
热度：37
Daisuke Ikeda(东京大学) We consider mining unusual patterns from text T. Unlike existing methods which assume probabilistic models and use simple estimation methods, we emplo...
热度：37