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KDD
2004
ACM
237views Data Mining» more  KDD 2004»
15 years 10 months ago
Bayesian Model-Averaging in Unsupervised Learning From Microarray Data
Unsupervised identification of patterns in microarray data has been a productive approach to uncovering relationships between genes and the biological process in which they are in...
Mario Medvedovic, Junhai Guo
PKDD
1999
Springer
90views Data Mining» more  PKDD 1999»
15 years 2 months ago
Learning from Highly Structured Data by Decomposition
This paper addresses the problem of learning from highly structured data. Speci cally, it describes a procedure, called decomposition, that allows a learner to access automatically...
René MacKinney-Romero, Christophe G. Giraud...
SDM
2004
SIAM
123views Data Mining» more  SDM 2004»
14 years 11 months ago
Nonlinear Manifold Learning for Data Stream
There has been a renewed interest in understanding the structure of high dimensional data set based on manifold learning. Examples include ISOMAP [25], LLE [20] and Laplacian Eige...
Martin H. C. Law, Nan Zhang 0002, Anil K. Jain
KDD
1994
ACM
123views Data Mining» more  KDD 1994»
15 years 2 months ago
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
We describe scoring metrics for learning Bayesian networks from a combination of user knowledge and statistical data. We identify two important properties of metrics, which we cal...
David Heckerman, Dan Geiger, David Maxwell Chicker...
ICDM
2005
IEEE
135views Data Mining» more  ICDM 2005»
15 years 3 months ago
Bit Reduction Support Vector Machine
Abstract— Support vector machines are very accurate classifiers and have been widely used in many applications. However, the training and to a lesser extent prediction time of s...
Tong Luo, Lawrence O. Hall, Dmitry B. Goldgof, And...