图 书 馆开课单位--中国科学院
11
12
13
14
15
11Beating Bandits in Gradually Evolving Worlds[在逐步演变的世界中击败Bandits]
Chia-Jung Lee(中国科学院) Consider the online convex optimization problem, in which a player has to choose actions iteratively and suffers corresponding losses according to som...
热度：47
Chia-Jung Lee(中国科学院) Consider the online convex optimization problem, in which a player has to choose actions iteratively and suffers corresponding losses according to som...
热度：47
12Extending logic programs with description logic expressions for the semantic wec[使用语义wec的描述逻辑表达式扩展逻辑程序]
Yi Dong Shen(中国科学院) Recently much attention has been directed to extending logic programming with description logic (DL) expressions, so that logic pro- grams have access...
热度：25
Yi Dong Shen(中国科学院) Recently much attention has been directed to extending logic programming with description logic (DL) expressions, so that logic pro- grams have access...
热度：25
13Robust Feature Extraction via Information Theoretic Learning[基于信息理论学习的鲁棒特征提取]
Xiao-Tong Yuan(中国科学院) In this paper, we present a robust feature extraction framework based on information-theoretic learning. Its formulated objective aims at dual target...
热度：105
Xiao-Tong Yuan(中国科学院) In this paper, we present a robust feature extraction framework based on information-theoretic learning. Its formulated objective aims at dual target...
热度：105
14Subspace Regularization: A New Semi-Supervised Learning Method[子空间正则化：一种新的半监督学习方法]
Yan-Ming Zhang(中国科学院) Most existing semi-supervised learning methods are based on the smoothness assumption that data points in the same high density region should have the...
热度：64
Yan-Ming Zhang(中国科学院) Most existing semi-supervised learning methods are based on the smoothness assumption that data points in the same high density region should have the...
热度：64
15An l1 Regularization Framework for Optimal Rule Combination[最优规则组合的l1正则化框架]
Yanjun Han(中国科学院) In this paper l1 regularization is introduced into relational learning to produce sparse rule combination. In other words, as few as possible rules ar...
热度：68
Yanjun Han(中国科学院) In this paper l1 regularization is introduced into relational learning to produce sparse rule combination. In other words, as few as possible rules ar...
热度：68