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» The Characterization of Data-Accumulating Algorithms
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SIGMOD
2007
ACM
181views Database» more  SIGMOD 2007»
16 years 4 months ago
Progressive and selective merge: computing top-k with ad-hoc ranking functions
The family of threshold algorithm (i.e., TA) has been widely studied for efficiently computing top-k queries. TA uses a sort-merge framework that assumes data lists are pre-sorted...
Dong Xin, Jiawei Han, Kevin Chen-Chuan Chang
CIAC
2010
Springer
275views Algorithms» more  CIAC 2010»
16 years 1 months ago
Online Cooperative Cost Sharing
The problem of sharing the cost of a common infrastructure among a set of strategic and cooperating players has been the subject of intensive research in recent years. However, mos...
Janina Brenner and Guido Schaefer
PAKDD
2009
ACM
209views Data Mining» more  PAKDD 2009»
16 years 1 months ago
Approximate Spectral Clustering.
Spectral clustering refers to a flexible class of clustering procedures that can produce high-quality clusterings on small data sets but which has limited applicability to large-...
Christopher Leckie, James C. Bezdek, Kotagiri Rama...
ALT
2007
Springer
16 years 23 days ago
Learning Kernel Perceptrons on Noisy Data Using Random Projections
In this paper, we address the issue of learning nonlinearly separable concepts with a kernel classifier in the situation where the data at hand are altered by a uniform classific...
Guillaume Stempfel, Liva Ralaivola
CVPR
2010
IEEE
16 years 1 hour ago
Learning Shift-Invariant Sparse Representation of Actions
A central problem in the analysis of motion capture (Mo- Cap) data is how to decompose motion sequences into primitives. Ideally, a description in terms of primitives should fac...
Yi Li