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» Top-k learning to rank: labeling, ranking and evaluation
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ICMCS
2008
IEEE
145views Multimedia» more  ICMCS 2008»
15 years 4 months ago
Video search reranking via online ordinal reranking
To exploit co-occurrence patterns among features and target semantics while keeping the simplicity of the keywordbased visual search, a novel reranking methods is proposed. The ap...
Yi-Hsuan Yang, Winston H. Hsu
ML
2010
ACM
124views Machine Learning» more  ML 2010»
14 years 8 months ago
Large scale image annotation: learning to rank with joint word-image embeddings
Image annotation datasets are becoming larger and larger, with tens of millions of images and tens of thousands of possible annotations. We propose a strongly performing method tha...
Jason Weston, Samy Bengio, Nicolas Usunier
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...
DATAMINE
2006
139views more  DATAMINE 2006»
14 years 9 months ago
VizRank: Data Visualization Guided by Machine Learning
Data visualization plays a crucial role in identifying interesting patterns in exploratory data analysis. Its use is, however, made difficult by the large number of possible data p...
Gregor Leban, Blaz Zupan, Gaj Vidmar, Ivan Bratko
ICALT
2010
IEEE
14 years 8 months ago
Course Ranking and Automated Suggestions through Web Mining
—This paper introduces new metrics for course evaluation. It is also proposes a ranking algorithm that classifies courses based on the previous course evaluation metrics and sugg...
Stavros Valsamidis, Ioannis Kazanidis, Sotirios Ko...