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ICML
2004
IEEE
16 years 5 months ago
Kernel conditional random fields: representation and clique selection
Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models...
John D. Lafferty, Xiaojin Zhu, Yan Liu
ICDM
2006
IEEE
152views Data Mining» more  ICDM 2006»
15 years 10 months ago
Application of Graph-based Data Mining to Metabolic Pathways
We present a method for finding biologically meaningful patterns on metabolic pathways using the SUBDUE graph-based relational learning system. A huge amount of biological data t...
Chang Hun You, Lawrence B. Holder, Diane J. Cook
ML
2010
ACM
141views Machine Learning» more  ML 2010»
15 years 2 months ago
Relational retrieval using a combination of path-constrained random walks
Scientific literature with rich metadata can be represented as a labeled directed graph. This graph representation enables a number of scientific tasks such as ad hoc retrieval o...
Ni Lao, William W. Cohen
PKDD
2009
Springer
117views Data Mining» more  PKDD 2009»
15 years 11 months ago
New Regularized Algorithms for Transductive Learning
Abstract. We propose a new graph-based label propagation algorithm for transductive learning. Each example is associated with a vertex in an undirected graph and a weighted edge be...
Partha Pratim Talukdar, Koby Crammer
ECML
2006
Springer
15 years 8 months ago
Multiple-Instance Learning Via Random Walk
This paper presents a decoupled two stage solution to the multiple-instance learning (MIL) problem. With a constructed affinity matrix to reflect the instance relations, a modified...
Dong Wang, Jianmin Li, Bo Zhang