图 书 馆开课单位--加州大学伯克利分校
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51Seeking Interpretable Models for High Dimensional Data[寻找高维数据的可解释模型]
Bin Yu(加州大学伯克利分校) Extracting useful information from high-dimensional data is the focus of today's statistical research and practice. After broad success of statistical...
热度：76
Bin Yu(加州大学伯克利分校) Extracting useful information from high-dimensional data is the focus of today's statistical research and practice. After broad success of statistical...
热度：76
52AdaBoost is Universally Consistent[演算法是普遍一致的]
Peter L. Bartlett(加州大学伯克利分校) We consider the risk, or probability of error, of the classifier produced by AdaBoost, and in particular the stopping strategy to be used to ensure un...
热度：41
Peter L. Bartlett(加州大学伯克利分校) We consider the risk, or probability of error, of the classifier produced by AdaBoost, and in particular the stopping strategy to be used to ensure un...
热度：41
53Inference for Networks[对网络的推断]
Peter J. Bickel(加州大学伯克利分校) A great deal of attention has recently been paid to determining sub-communities on the basis of relations, corresponding to edges, between individuals...
热度：36
Peter J. Bickel(加州大学伯克利分校) A great deal of attention has recently been paid to determining sub-communities on the basis of relations, corresponding to edges, between individuals...
热度：36
54Pattern Classification and Large Margin Classifiers[模式分类和大边距分类器]
Peter L. Bartlett(加州大学伯克利分校) These lectures will provide an introduction to the theory of pattern classification methods. They will focus on relationships between the minimax perf...
热度：75
Peter L. Bartlett(加州大学伯克利分校) These lectures will provide an introduction to the theory of pattern classification methods. They will focus on relationships between the minimax perf...
热度：75
55On the Borders of Statistics and Computer Science[论统计与计算机科学的边界]
Peter J. Bickel(加州大学伯克利分校) Machine learning in computer science and prediction and classification in statistics are essentially equivalent fields. I will try to illustrate the r...
热度：62
Peter J. Bickel(加州大学伯克利分校) Machine learning in computer science and prediction and classification in statistics are essentially equivalent fields. I will try to illustrate the r...
热度：62
56On the Adequacy of Baseform Pronunciations and Pronunciation Variants[论基线发音和发音变体的充分性]
Chuck Wooters(加州大学伯克利分校) This paper presents an approach to automatically extract and evaluate the ``stability'' of pronunciation variants (i.e., adequacy of the model to acco...
热度：62
Chuck Wooters(加州大学伯克利分校) This paper presents an approach to automatically extract and evaluate the ``stability'' of pronunciation variants (i.e., adequacy of the model to acco...
热度：62
57Horizon-Independent Optimal Prediction with Log-Loss in Exponential Families[指数族中具有对数损失水平的独立最优预测]
Fares Hedayati(加州大学伯克利分校) We study online learning under logarithmic loss with regular parametric models. Hedayati and Bartlett (2012) showed that a Bayesian prediction strateg...
热度：40
Fares Hedayati(加州大学伯克利分校) We study online learning under logarithmic loss with regular parametric models. Hedayati and Bartlett (2012) showed that a Bayesian prediction strateg...
热度：40
58Machine Learning for Robotics[机器人的学习能力]
Pieter Abbeel(加州大学伯克利分校) Robots are typically far less capable in autonomous mode than in tele-operated mode. The few exceptions tend to stem from long days (and more often we...
热度：119
Pieter Abbeel(加州大学伯克利分校) Robots are typically far less capable in autonomous mode than in tele-operated mode. The few exceptions tend to stem from long days (and more often we...
热度：119
59Near Optimal Chernoff Bounds for Markov Decision Processes[马尔可夫决策过程的近最佳Chernoff界]
Teodor Mihai Moldovan(加州大学伯克利分校) The expected return is a widely used objective in decision making under uncertainty. Many algorithms, such as value iteration, have been proposed to o...
热度：57
Teodor Mihai Moldovan(加州大学伯克利分校) The expected return is a widely used objective in decision making under uncertainty. Many algorithms, such as value iteration, have been proposed to o...
热度：57
60Group Lasso with Overlaps and Graph Lasso[使用Overlaps和Graph Lasso组合套索]
Laurent Jacob(加州大学伯克利分校) We propose a new penalty function which, when used as regularization for empirical risk minimization procedures, leads to sparse estimators. The suppo...
热度：89
Laurent Jacob(加州大学伯克利分校) We propose a new penalty function which, when used as regularization for empirical risk minimization procedures, leads to sparse estimators. The suppo...
热度：89