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» Using Supervised Clustering to Enhance Classifiers
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SIGIR
2002
ACM
13 years 7 months ago
Unsupervised document classification using sequential information maximization
We present a novel sequential clustering algorithm which is motivated by the Information Bottleneck (IB) method. In contrast to the agglomerative IB algorithm, the new sequential ...
Noam Slonim, Nir Friedman, Naftali Tishby
BMCBI
2008
167views more  BMCBI 2008»
13 years 7 months ago
Expression profiles of switch-like genes accurately classify tissue and infectious disease phenotypes in model-based classificat
Background: Large-scale compilation of gene expression microarray datasets across diverse biological phenotypes provided a means of gathering a priori knowledge in the form of ide...
Michael Gormley, Aydin Tozeren
ISBRA
2007
Springer
14 years 1 months ago
Wavelet Image Interpolation (WII): A Wavelet-Based Approach to Enhancement of Digital Mammography Images
Abstract. Cancer detection using mammography focuses on characteristics of tiny microcalcifications, including the number, size, and spatial arrangement of microcalcification clu...
Gordana Derado, F. DuBois Bowman, Rajan Patel, Mar...
KDD
2007
ACM
152views Data Mining» more  KDD 2007»
14 years 7 months ago
Privacy-Preserving Sharing of Horizontally-Distributed Private Data for Constructing Accurate Classifiers
Data mining tasks such as supervised classification can often benefit from a large training dataset. However, in many application domains, privacy concerns can hinder the construc...
Vincent Yan Fu Tan, See-Kiong Ng
JEI
2008
307views more  JEI 2008»
13 years 7 months ago
Recall or precision-oriented strategies for binary classification of skin pixels
Skin detection is a preliminary step in many applications. We analyze some of the most frequently cited binary skin classifiers based on explicit color cluster definition and prese...
Francesca Gasparini, Silvia Corchs, Raimondo Schet...