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NIPS
2007
14 years 11 months ago
A General Boosting Method and its Application to Learning Ranking Functions for Web Search
We present a general boosting method extending functional gradient boosting to optimize complex loss functions that are encountered in many machine learning problems. Our approach...
Zhaohui Zheng, Hongyuan Zha, Tong Zhang, Olivier C...
WWW
2010
ACM
15 years 4 months ago
iRIN: image retrieval in image-rich information networks
In this demo, we present a system called iRIN designed for performing image retrieval in image-rich information networks. We first introduce MoK-SimRank to significantly improve...
Xin Jin, Jiebo Luo, Jie Yu, Gang Wang, Dhiraj Josh...
COLING
2010
14 years 4 months ago
Cross-Market Model Adaptation with Pairwise Preference Data for Web Search Ranking
Machine-learned ranking techniques automatically learn a complex document ranking function given training data. These techniques have demonstrated the effectiveness and flexibilit...
Jing Bai, Fernando Diaz, Yi Chang, Zhaohui Zheng, ...
IJON
2010
139views more  IJON 2010»
14 years 8 months ago
Event monitoring via local motion abnormality detection in non-linear subspace
We present a computational approach to abnormal visual event detection, which is based on exploring and modeling local motion patterns in a non-linear subspace. We use motion vect...
Ioannis Tziakos, Andrea Cavallaro, Li-Qun Xu
KDD
2005
ACM
177views Data Mining» more  KDD 2005»
15 years 10 months ago
Query chains: learning to rank from implicit feedback
This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform ...
Filip Radlinski, Thorsten Joachims