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» On Kernel Methods for Relational Learning
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ICML
2009
IEEE
14 years 8 months ago
Multi-instance learning by treating instances as non-I.I.D. samples
Previous studies on multi-instance learning typically treated instances in the bags as independently and identically distributed. The instances in a bag, however, are rarely indep...
Zhi-Hua Zhou, Yu-Yin Sun, Yu-Feng Li
ACL
2003
13 years 9 months ago
Hierarchical Directed Acyclic Graph Kernel: Methods for Structured Natural Language Data
This paper proposes the “Hierarchical Directed Acyclic Graph (HDAG) Kernel” for structured natural language data. The HDAG Kernel directly accepts several levels of both chunk...
Jun Suzuki, Tsutomu Hirao, Yutaka Sasaki, Eisaku M...
ICDM
2009
IEEE
174views Data Mining» more  ICDM 2009»
14 years 2 months ago
Non-sparse Multiple Kernel Learning for Fisher Discriminant Analysis
—We consider the problem of learning a linear combination of pre-specified kernel matrices in the Fisher discriminant analysis setting. Existing methods for such a task impose a...
Fei Yan, Josef Kittler, Krystian Mikolajczyk, Muha...
JMLR
2006
112views more  JMLR 2006»
13 years 7 months ago
Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting
We develop kernels for measuring the similarity between relational instances using background knowledge expressed in first-order logic. The method allows us to bridge the gap betw...
Andrea Passerini, Paolo Frasconi, Luc De Raedt
SDM
2004
SIAM
218views Data Mining» more  SDM 2004»
13 years 9 months ago
Mixture Density Mercer Kernels: A Method to Learn Kernels Directly from Data
This paper presents a method of generating Mercer Kernels from an ensemble of probabilistic mixture models, where each mixture model is generated from a Bayesian mixture density e...
Ashok N. Srivastava