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» Data Mining: Machine Learning, Statistics, and Databases
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KDD
2008
ACM
119views Data Mining» more  KDD 2008»
16 years 2 months ago
SAIL: summation-based incremental learning for information-theoretic clustering
Information-theoretic clustering aims to exploit information theoretic measures as the clustering criteria. A common practice on this topic is so-called INFO-K-means, which perfor...
Junjie Wu, Hui Xiong, Jian Chen
ICML
2007
IEEE
16 years 2 months ago
Bottom-up learning of Markov logic network structure
Markov logic networks (MLNs) are a statistical relational model that consists of weighted firstorder clauses and generalizes first-order logic and Markov networks. The current sta...
Lilyana Mihalkova, Raymond J. Mooney
149
Voted
KDD
2007
ACM
132views Data Mining» more  KDD 2007»
16 years 2 months ago
A scalable modular convex solver for regularized risk minimization
A wide variety of machine learning problems can be described as minimizing a regularized risk functional, with different algorithms using different notions of risk and different r...
Choon Hui Teo, Alex J. Smola, S. V. N. Vishwanatha...
110
Voted
ICAI
2004
15 years 3 months ago
Inductive System Health Monitoring
- The Inductive Monitoring System (IMS) software was developed to provide a technique to automatically produce health monitoring knowledge bases for systems that are either difficu...
David L. Iverson
ICML
2006
IEEE
16 years 2 months ago
Efficient learning of Naive Bayes classifiers under class-conditional classification noise
We address the problem of efficiently learning Naive Bayes classifiers under classconditional classification noise (CCCN). Naive Bayes classifiers rely on the hypothesis that the ...
Christophe Nicolas Magnan, François Denis, ...