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» On Kernel Methods for Relational Learning
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NIPS
2008
15 years 4 months ago
Posterior Consistency of the Silverman g-prior in Bayesian Model Choice
Kernel supervised learning methods can be unified by utilizing the tools from regularization theory. The duality between regularization and prior leads to interpreting regularizat...
Zhihua Zhang, Michael I. Jordan, Dit-Yan Yeung
BIOINFORMATICS
2005
140views more  BIOINFORMATICS 2005»
15 years 3 months ago
Profile-based direct kernels for remote homology detection and fold recognition
Motivation: Remote homology detection between protein sequences is a central problem in computational biology. Supervised learning algorithms based on support vector machines are ...
Huzefa Rangwala, George Karypis
ACL
2007
15 years 4 months ago
A Seed-driven Bottom-up Machine Learning Framework for Extracting Relations of Various Complexity
A minimally supervised machine learning framework is described for extracting relations of various complexity. Bootstrapping starts from a small set of n-ary relation instances as...
Feiyu Xu, Hans Uszkoreit, Hong Li
ICML
2003
IEEE
16 years 3 months ago
Identifying Predictive Structures in Relational Data Using Multiple Instance Learning
This paper introduces an approach for identifying predictive structures in relational data using the multiple-instance framework. By a predictive structure, we mean a structure th...
Amy McGovern, David Jensen
ML
2006
ACM
110views Machine Learning» more  ML 2006»
15 years 3 months ago
Distribution-based aggregation for relational learning with identifier attributes
Abstract Identifier attributes--very high-dimensional categorical attributes such as particular product ids or people's names--rarely are incorporated in statistical modeling....
Claudia Perlich, Foster J. Provost