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» Benchmarking Data Mining Algorithms
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KDD
2004
ACM
187views Data Mining» more  KDD 2004»
16 years 5 months ago
IMMC: incremental maximum margin criterion
Subspace learning approaches have attracted much attention in academia recently. However, the classical batch algorithms no longer satisfy the applications on streaming data or la...
Jun Yan, Benyu Zhang, Shuicheng Yan, Qiang Yang, H...
ICDM
2006
IEEE
119views Data Mining» more  ICDM 2006»
15 years 11 months ago
Fast On-line Kernel Learning for Trees
Kernel methods have been shown to be very effective for applications requiring the modeling of structured objects. However kernels for structures usually are too computational dem...
Fabio Aiolli, Giovanni Da San Martino, Alessandro ...
141
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MCS
2006
Springer
15 years 5 months ago
Variable projections neural network training
8 The training of some types of neural networks leads to separable non-linear least squares problems. These problems may be9 ill-conditioned and require special techniques. A robus...
V. Pereyra, G. Scherer, F. Wong
KDD
2007
ACM
376views Data Mining» more  KDD 2007»
16 years 5 months ago
Truth discovery with multiple conflicting information providers on the web
The world-wide web has become the most important information source for most of us. Unfortunately, there is no guarantee for the correctness of information on the web. Moreover, d...
Xiaoxin Yin, Jiawei Han, Philip S. Yu
KDD
2006
ACM
136views Data Mining» more  KDD 2006»
16 years 5 months ago
Very sparse random projections
There has been considerable interest in random projections, an approximate algorithm for estimating distances between pairs of points in a high-dimensional vector space. Let A Rn...
Ping Li, Trevor Hastie, Kenneth Ward Church