图 书 馆开课单位--伦敦大学学院
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101Can Style be Learned? A Machine Learning Approach Towards ‘Performing’ as Famous Pianists[风格可以学到什么？一种针对著名钢琴家
Louis Dorard(伦敦大学学院) In this paper a novel method for performing music in the style of famous pianists is presented. We use Kernel Canonical Correlation Analysis (KCCA), a...
热度：47
Louis Dorard(伦敦大学学院) In this paper a novel method for performing music in the style of famous pianists is presented. We use Kernel Canonical Correlation Analysis (KCCA), a...
热度：47
102PAC-Bayes Analysis: Links to Luckiness and Applications[PAC的Bayes分析：以吉祥、应用的链接]
John Shawe-Taylor(伦敦大学学院)
热度：42
John Shawe-Taylor(伦敦大学学院)
热度：42
103104Multimodal Processing and Multimedia Understanding: Image Retrieval Using Eye Movements[多模态处理与多媒体理解：眼球运动的图像检索]
Fred Stentiford(伦敦大学学院) His presentation describes experiments that explored eye behaviour when carrying out purely visual tasks on a Corel database of 1000 images. Results a...
热度：45
Fred Stentiford(伦敦大学学院) His presentation describes experiments that explored eye behaviour when carrying out purely visual tasks on a Corel database of 1000 images. Results a...
热度：45
105Directed Graphical Models[定向图形模型]
Cedric Archambeau(伦敦大学学院) In this talk I introduce the basic concepts of directed graphical models. I then introduce the EM algorithm and discuss learning in latent variable mo...
热度：48
Cedric Archambeau(伦敦大学学院) In this talk I introduce the basic concepts of directed graphical models. I then introduce the EM algorithm and discuss learning in latent variable mo...
热度：48
106The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling, incl. discussion by Frank Wood[高阶正则化的半监督学习]
John Paisley, Frank Wood(伦敦大学学院) We present the discrete infinite logistic normal distribution (DILN, “Dylan”), a Bayesian nonparametric prior for mixed membership models....
热度：39
John Paisley, Frank Wood(伦敦大学学院) We present the discrete infinite logistic normal distribution (DILN, “Dylan”), a Bayesian nonparametric prior for mixed membership models....
热度：39
107Statistical Significance and Stability Analysis for Patterns[统计显著性和稳定性分析模式]
John Shawe-Taylor(伦敦大学学院) 统计显著性和稳定性分析模式
热度：42
John Shawe-Taylor(伦敦大学学院) 统计显著性和稳定性分析模式
热度：42
108109Bayesian Probabilistic Models for Image Retrieval[图像检索的贝叶斯概率模型]
Vassilios Stathopoulos(伦敦大学学院) In this paper we present new probabilistic ranking functions for content based image retrieval. Our methodology generalises previous approaches and is...
热度：38
Vassilios Stathopoulos(伦敦大学学院) In this paper we present new probabilistic ranking functions for content based image retrieval. Our methodology generalises previous approaches and is...
热度：38
110Support Vector and Kernel Methods[支持向量与内核方法]
John Shawe-Taylor(伦敦大学学院) The lectures will introduce the kernel methods approach to pattern analysis through the particular example of support vector machines for classificati...
热度：45
John Shawe-Taylor(伦敦大学学院) The lectures will introduce the kernel methods approach to pattern analysis through the particular example of support vector machines for classificati...
热度：45