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KDD
2003
ACM
127views Data Mining» more  KDD 2003»
14 years 10 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
KDD
2002
ACM
108views Data Mining» more  KDD 2002»
14 years 10 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
PKDD
2009
Springer
118views Data Mining» more  PKDD 2009»
14 years 4 months ago
Sparse Kernel SVMs via Cutting-Plane Training
We explore an algorithm for training SVMs with Kernels that can represent the learned rule using arbitrary basis vectors, not just the support vectors (SVs) from the training set. ...
Thorsten Joachims, Chun-Nam John Yu
ICDM
2007
IEEE
140views Data Mining» more  ICDM 2007»
14 years 4 months ago
Finding Cohesive Clusters for Analyzing Knowledge Communities
Documents and authors can be clustered into “knowledge communities” based on the overlap in the papers they cite. We introduce a new clustering algorithm, Streemer, which fin...
Vasileios Kandylas, S. Phineas Upham, Lyle H. Unga...
AUSDM
2007
Springer
107views Data Mining» more  AUSDM 2007»
14 years 4 months ago
Preference Networks: Probabilistic Models for Recommendation Systems
Recommender systems are important to help users select relevant and personalised information over massive amounts of data available. We propose an unified framework called Prefer...
Tran The Truyen, Dinh Q. Phung, Svetha Venkatesh