图 书 馆开课单位--加州大学圣地亚哥分校
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21Strategic Impatience in Go/NoGo versus Forced-Choice Decision-Making[go/nogo中的战略不耐烦与强迫选择决策 ]
Angela J. Yu(加州大学圣地亚哥分校) Two-alternative forced choice (2AFC) and Go/NoGo (GNG) tasks are behavioral choice paradigms commonly used to study sensory and cognitive processing i...
热度：118
Angela J. Yu(加州大学圣地亚哥分校) Two-alternative forced choice (2AFC) and Go/NoGo (GNG) tasks are behavioral choice paradigms commonly used to study sensory and cognitive processing i...
热度：118
22Granger Causality and Dynamic Structural Systems[格兰杰因果关系与动态结构系统]
Halbert White(加州大学圣地亚哥分校) Using a generally applicable dynamic structural system of equations, we give natural definitions of direct and total structural causality applicable t...
热度：88
Halbert White(加州大学圣地亚哥分校) Using a generally applicable dynamic structural system of equations, we give natural definitions of direct and total structural causality applicable t...
热度：88
23Multiplicative Updates for L1-Regularized Linear and Logistic Regression[L1正则线性回归和逻辑回归的乘法更新 ]
Lawrence Saul(加州大学圣地亚哥分校 ) Multiplicative update rules have proven useful in many areas of machine learning. Simple to implement, guaranteed to converge, they account in part ...
热度：192
Lawrence Saul(加州大学圣地亚哥分校 ) Multiplicative update rules have proven useful in many areas of machine learning. Simple to implement, guaranteed to converge, they account in part ...
热度：192
24Independent Factor Topic Models[独立因素主题模型]
Duangmanee (Pew) Putthividhya(加州大学圣地亚哥分校) Topic models such as Latent Dirichlet Allocation (LDA) and Correlated Topic Model (CTM) have recently emerged as powerful statistical tools for text ...
热度：22
Duangmanee (Pew) Putthividhya(加州大学圣地亚哥分校) Topic models such as Latent Dirichlet Allocation (LDA) and Correlated Topic Model (CTM) have recently emerged as powerful statistical tools for text ...
热度：22
25Partial Order Embedding with Multiple Kernels[具有多个内核的部分顺序嵌入]
Brian McFee(加州大学圣地亚哥分校) We consider the problem of embedding arbitrary objects (e.g., images, audio, documents) into Euclidean space subject to a partial order over pairwise ...
热度：49
Brian McFee(加州大学圣地亚哥分校) We consider the problem of embedding arbitrary objects (e.g., images, audio, documents) into Euclidean space subject to a partial order over pairwise ...
热度：49
26Multi-View Clustering via Canonical Correlation Analysis[基于典型相关分析的多视图聚类]
Kamalika Chaudhuri(加州大学圣地亚哥分校) Clustering data in high dimensions is believed to be a hard problem in general. A number of efficient clustering algorithms developed in recent years ...
热度：250
Kamalika Chaudhuri(加州大学圣地亚哥分校) Clustering data in high dimensions is believed to be a hard problem in general. A number of efficient clustering algorithms developed in recent years ...
热度：250
27Boosting with the Logistic Loss is Consistent[提升物流损失是一致的]
Matus Telgarsky(加州大学圣地亚哥分校) This manuscript provides optimization guarantees, generalization bounds, and statistical consistency results for AdaBoost variants which replace the e...
热度：51
Matus Telgarsky(加州大学圣地亚哥分校) This manuscript provides optimization guarantees, generalization bounds, and statistical consistency results for AdaBoost variants which replace the e...
热度：51
28Competitive Closeness Testing[竞争性亲密度测试]
Hirakendu Das(加州大学圣地亚哥分校) We test whether two sequences are generated by the same distribution or by two diff erent ones. Unlike previous work, we make no assumptions on the di...
热度：57
Hirakendu Das(加州大学圣地亚哥分校) We test whether two sequences are generated by the same distribution or by two diff erent ones. Unlike previous work, we make no assumptions on the di...
热度：57
29Optimal Probability Estimation with Applications to Prediction and Classification[最优概率估计及其在预测分类中的应用]
Ananda Theertha Suresh(加州大学圣地亚哥分校) Via a unified viewpoint of probability estimation, classification,and prediction, we derive a uniformly-optimal combined-probability estimator, constr...
热度：40
Ananda Theertha Suresh(加州大学圣地亚哥分校) Via a unified viewpoint of probability estimation, classification,and prediction, we derive a uniformly-optimal combined-probability estimator, constr...
热度：40