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PPSN
1990
Springer
15 years 2 months ago
Feature Construction for Back-Propagation
T h e ease of learning concepts f r o m examples in empirical machine learning depends on the attributes used for describing the training d a t a . We show t h a t decision-tree b...
Selwyn Piramuthu
90
Voted
CVPR
2005
IEEE
16 years 2 days ago
Local Color Transfer via Probabilistic Segmentation by Expectation-Maximization
We address the problem of regional color transfer between two natural images by probabilistic segmentation. We use a new Expectation-Maximization (EM) scheme to impose both spatia...
Yu-Wing Tai, Jiaya Jia, Chi-Keung Tang
WWW
2009
ACM
15 years 10 months ago
Enhancing diversity, coverage and balance for summarization through structure learning
Document summarization plays an increasingly important role with the exponential growth of documents on the Web. Many supervised and unsupervised approaches have been proposed to ...
Liangda Li, Ke Zhou, Gui-Rong Xue, Hongyuan Zha, Y...
KDD
2009
ACM
249views Data Mining» more  KDD 2009»
15 years 10 months ago
Drosophila gene expression pattern annotation using sparse features and term-term interactions
The Drosophila gene expression pattern images document the spatial and temporal dynamics of gene expression and they are valuable tools for explicating the gene functions, interac...
Shuiwang Ji, Lei Yuan, Ying-Xin Li, Zhi-Hua Zhou, ...
KDD
2009
ACM
163views Data Mining» more  KDD 2009»
15 years 10 months ago
Large-scale graph mining using backbone refinement classes
We present a new approach to large-scale graph mining based on so-called backbone refinement classes. The method efficiently mines tree-shaped subgraph descriptors under minimum f...
Andreas Maunz, Christoph Helma, Stefan Kramer