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ECML
2001
Springer
14 years 2 days ago
Iterative Double Clustering for Unsupervised and Semi-supervised Learning
We present a powerful meta-clustering technique called Iterative Double Clustering (IDC). The IDC method is a natural extension of the recent Double Clustering (DC) method of Slon...
Ran El-Yaniv, Oren Souroujon
FOIKS
2008
Springer
14 years 4 months ago
Cost-minimising strategies for data labelling : optimal stopping and active learning
Supervised learning deals with the inference of a distribution over an output or label space $\CY$ conditioned on points in an observation space $\CX$, given a training dataset $D$...
Christos Dimitrakakis, Christian Savu-Krohn
JCP
2006
118views more  JCP 2006»
13 years 7 months ago
Learning a Classification-based Glioma Growth Model Using MRI Data
Gliomas are malignant brain tumors that grow by invading adjacent tissue. We propose and evaluate a 3D classification-based growth model, CDM, that predicts how a glioma will grow ...
Marianne Morris, Russell Greiner, Jörg Sander...
CVPR
2010
IEEE
14 years 4 months ago
Learning Mid-Level Features For Recognition
Many successful models for scene or object recognition transform low-level descriptors (such as Gabor filter responses, or SIFT descriptors) into richer representations of interme...
Y-Lan Boureau, Francis Bach, Yann LeCun, Jean Ponc...
LCPC
2005
Springer
14 years 1 months ago
Analytic Models and Empirical Search: A Hybrid Approach to Code Optimization
Compilers employ system models, sometimes implicitly, to make code optimization decisions. These models are analytic; they reflect their implementor’s understanding and beliefs ...
Arkady Epshteyn, María Jesús Garzar&...