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ML
2002
ACM
163views Machine Learning» more  ML 2002»
15 years 6 months ago
Structural Modelling with Sparse Kernels
A widely acknowledged drawback of many statistical modelling techniques, commonly used in machine learning, is that the resulting model is extremely difficult to interpret. A numb...
Steve R. Gunn, Jaz S. Kandola
IJCV
2000
164views more  IJCV 2000»
15 years 6 months ago
Probabilistic Modeling and Recognition of 3-D Objects
This paper introduces a uniform statistical framework for both 3-D and 2-D object recognition using intensity images as input data. The theoretical part provides a mathematical too...
Joachim Hornegger, Heinrich Niemann
JMLR
2002
138views more  JMLR 2002»
15 years 6 months ago
Learning Probabilistic Models of Link Structure
Most real-world data is heterogeneous and richly interconnected. Examples include the Web, hypertext, bibliometric data and social networks. In contrast, most statistical learning...
Lise Getoor, Nir Friedman, Daphne Koller, Benjamin...
JMLR
2010
169views more  JMLR 2010»
15 years 1 months ago
Matrix-Variate Dirichlet Process Mixture Models
We are concerned with a multivariate response regression problem where the interest is in considering correlations both across response variates and across response samples. In th...
Zhihua Zhang, Guang Dai, Michael I. Jordan
WAW
2010
Springer
231views Algorithms» more  WAW 2010»
15 years 4 months ago
Modeling Traffic on the Web Graph
Abstract. Analysis of aggregate and individual Web requests shows that PageRank is a poor predictor of traffic. We use empirical data to characterize properties of Web traffic not ...
Mark R. Meiss, Bruno Gonçalves, Jose J. Ram...