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» Data mining, Hypergraph Transversals, and Machine Learning
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KDD
1997
ACM
109views Data Mining» more  KDD 1997»
14 years 17 days ago
Beyond Concise and Colorful: Learning Intelligible Rules
A variety of techniques from statistics, signal processing, pattern recognition, machine learning, and neural networks have been proposed to understand data by discovering useful ...
Michael J. Pazzani, Subramani Mani, William Rodman...
ICDM
2009
IEEE
172views Data Mining» more  ICDM 2009»
14 years 3 months ago
Sparse Least-Squares Methods in the Parallel Machine Learning (PML) Framework
—We describe parallel methods for solving large-scale, high-dimensional, sparse least-squares problems that arise in machine learning applications such as document classificatio...
Ramesh Natarajan, Vikas Sindhwani, Shirish Tatikon...
KDD
2002
ACM
108views Data Mining» more  KDD 2002»
14 years 8 months ago
Incremental Machine Learning to Reduce Biochemistry Lab Costs in the Search for Drug Discovery
This paper promotes the use of supervised machine learning in laboratory settings where chemists have a large number of samples to test for some property, and are interested in id...
George Forman
KDD
2003
ACM
127views Data Mining» more  KDD 2003»
14 years 8 months ago
Experiments with random projections for machine learning
Dimensionality reduction via Random Projections has attracted considerable attention in recent years. The approach has interesting theoretical underpinnings and offers computation...
Dmitriy Fradkin, David Madigan
PAKDD
2009
ACM
96views Data Mining» more  PAKDD 2009»
14 years 3 months ago
Aggregated Subset Mining
The usual data mining setting uses the full amount of data to derive patterns for different purposes. Taking cues from machine learning techniques, we explore ways to divide the d...
Albrecht Zimmermann, Björn Bringmann