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GRC
2010
IEEE
13 years 12 months ago
Learning Multiple Latent Variables with Self-Organizing Maps
Inference of latent variables from complicated data is one important problem in data mining. The high dimensionality and high complexity of real world data often make accurate infe...
Lili Zhang, Erzsébet Merényi
PKDD
2004
Springer
97views Data Mining» more  PKDD 2004»
14 years 4 months ago
Dealing with Predictive-but-Unpredictable Attributes in Noisy Data Sources
Attribute noise can affect classification learning. Previous work in handling attribute noise has focused on those predictable attributes that can be predicted by the class and o...
Ying Yang, Xindong Wu, Xingquan Zhu
AI
2006
Springer
14 years 2 months ago
A Classification-Based Glioma Diffusion Model Using MRI Data
Gliomas are diffuse, invasive brain tumors. We propose a 3D classification-based diffusion model, cdm, that predicts how a glioma will grow at a voxel-level, on the basis of featur...
Marianne Morris, Russell Greiner, Jörg Sander...
ACL
2010
13 years 8 months ago
Combining Data and Mathematical Models of Language Change
English noun/verb (N/V) pairs (contract, cement) have undergone complex patterns of change between 3 stress patterns for several centuries. We describe a longitudinal dataset of N...
Morgan Sonderegger, Partha Niyogi
SDM
2007
SIAM
182views Data Mining» more  SDM 2007»
14 years 5 days ago
Distance Preserving Dimension Reduction for Manifold Learning
Manifold learning is an effective methodology for extracting nonlinear structures from high-dimensional data with many applications in image analysis, computer vision, text data a...
Hyunsoo Kim, Haesun Park, Hongyuan Zha