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135
Voted
SDM
2007
SIAM
104views Data Mining» more  SDM 2007»
15 years 6 months ago
Fast Multilevel Transduction on Graphs
The recent years have witnessed a surge of interest in graphbased semi-supervised learning methods. The common denominator of these methods is that the data are represented by the...
Fei Wang, Changshui Zhang
KDD
2010
ACM
274views Data Mining» more  KDD 2010»
15 years 8 months ago
Grafting-light: fast, incremental feature selection and structure learning of Markov random fields
Feature selection is an important task in order to achieve better generalizability in high dimensional learning, and structure learning of Markov random fields (MRFs) can automat...
Jun Zhu, Ni Lao, Eric P. Xing
139
Voted
ICML
2001
IEEE
16 years 5 months ago
Learning to Generate Fast Signal Processing Implementations
A single signal processing algorithm can be represented by many mathematically equivalent formulas. However, when these formulas are implemented in code and run on real machines, ...
Bryan Singer, Manuela M. Veloso
KDD
2004
ACM
196views Data Mining» more  KDD 2004»
16 years 5 months ago
Adversarial classification
Essentially all data mining algorithms assume that the datagenerating process is independent of the data miner's activities. However, in many domains, including spam detectio...
Nilesh N. Dalvi, Pedro Domingos, Mausam, Sumit K. ...
CIKM
2010
Springer
15 years 2 months ago
Mining topic-level influence in heterogeneous networks
Influence is a complex and subtle force that governs the dynamics of social networks as well as the behaviors of involved users. Understanding influence can benefit various applic...
Lu Liu, Jie Tang, Jiawei Han, Meng Jiang, Shiqiang...