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157
Voted
KDD
2004
ACM
187views Data Mining» more  KDD 2004»
16 years 6 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...
143
Voted
KDD
2003
ACM
124views Data Mining» more  KDD 2003»
16 years 6 months ago
Information-theoretic co-clustering
Two-dimensional contingency or co-occurrence tables arise frequently in important applications such as text, web-log and market-basket data analysis. A basic problem in contingenc...
Inderjit S. Dhillon, Subramanyam Mallela, Dharmend...
KDD
2002
ACM
119views Data Mining» more  KDD 2002»
16 years 6 months ago
Evaluating classifiers' performance in a constrained environment
In this paper, we focus on methodology of finding a classifier with a minimal cost in presence of additional performance constraints. ROCCH analysis, where accuracy and cost are i...
Anna Olecka
KDD
2001
ACM
166views Data Mining» more  KDD 2001»
16 years 6 months ago
Generalized clustering, supervised learning, and data assignment
Clustering algorithms have become increasingly important in handling and analyzing data. Considerable work has been done in devising effective but increasingly specific clustering...
Annaka Kalton, Pat Langley, Kiri Wagstaff, Jungsoo...
PKDD
2009
Springer
138views Data Mining» more  PKDD 2009»
16 years 21 days ago
Margin and Radius Based Multiple Kernel Learning
A serious drawback of kernel methods, and Support Vector Machines (SVM) in particular, is the difficulty in choosing a suitable kernel function for a given dataset. One of the appr...
Huyen Do, Alexandros Kalousis, Adam Woznica, Melan...