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» Boosting with Diverse Base Classifiers
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CVPR
2004
IEEE
16 years 3 months ago
Learning Distance Functions for Image Retrieval
Image retrieval critically relies on the distance function used to compare a query image to images in the database. We suggest to learn such distance functions by training binary ...
Tomer Hertz, Aharon Bar-Hillel, Daphna Weinshall
CVPR
2008
IEEE
16 years 3 months ago
In defense of Nearest-Neighbor based image classification
State-of-the-art image classification methods require an intensive learning/training stage (using SVM, Boosting, etc.) In contrast, non-parametric Nearest-Neighbor (NN) based imag...
Oren Boiman, Eli Shechtman, Michal Irani
BIOINFORMATICS
2012
13 years 4 months ago
MolClass: a web portal to interrogate diverse small molecule screen datasets with different computational models
Summary: The MolClass toolkit and data portal generates computational models from user-defined small molecule datasets based on structural features identified in hit and non-hit m...
Jan Wildenhain, Nicholas FitzGerald, Mike Tyers
129
Voted
TMA
2010
Springer
140views Management» more  TMA 2010»
14 years 11 months ago
Uncovering Relations between Traffic Classifiers and Anomaly Detectors via Graph Theory
Abstract. Network traffic classification and anomaly detection have received much attention in the last few years. However, due to the the lack of common ground truth, proposed met...
Romain Fontugne, Pierre Borgnat, Patrice Abry, Ken...
VTC
2006
IEEE
15 years 7 months ago
Soft Detection with Linear Precoding for Spatial Multiplexing Systems
— We present a precoded reduced-complexity soft detection (PRCSD) algorithm for spatial multiplexing systems. The linear operations at both transmit and receive sides based on co...
Yong Li, Jaekyun Moon