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» Evaluating algorithms that learn from data streams
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KDD
2004
ACM
132views Data Mining» more  KDD 2004»
16 years 8 months ago
A probabilistic framework for semi-supervised clustering
Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clu...
Sugato Basu, Mikhail Bilenko, Raymond J. Mooney
ACISP
2005
Springer
16 years 1 months ago
Cancelable Key-Based Fingerprint Templates
Biometric based authentication can provide strong security guarantee about the identity of users. Security of biometric data is particularly important as compromise of the data wil...
Russell Ang, Reihaneh Safavi-Naini, Luke McAven
IEEEVAST
2010
15 years 2 months ago
Finding and visualizing relevant subspaces for clustering high-dimensional astronomical data using connected morphological opera
Data sets in astronomy are growing to enormous sizes. Modern astronomical surveys provide not only image data but also catalogues of millions of objects (stars, galaxies), each ob...
Bilkis J. Ferdosi, Hugo Buddelmeijer, Scott Trager...
ICCV
2005
IEEE
16 years 1 months ago
Image Based Regression Using Boosting Method
We present a general algorithm of image based regression that is applicable to many vision problems. The proposed regressor that targets a multiple-output setting is learned using...
Shaohua Kevin Zhou, Bogdan Georgescu, Xiang Sean Z...
ICDM
2007
IEEE
99views Data Mining» more  ICDM 2007»
16 years 1 months ago
Optimizing Frequency Queries for Data Mining Applications
Data mining algorithms use various Trie and bitmap-based representations to optimize the support (i.e., frequency) counting performance. In this paper, we compare the memory requi...
Hassan H. Malik, John R. Kender