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DEBU
2000
101views more  DEBU 2000»
15 years 4 months ago
Learning to Understand the Web
In a traditional information retrieval system, it is assumed that queries can be posed about any topic. In reality, a large fraction of web queries are posed about a relatively sm...
William W. Cohen, Andrew McCallum, Dallan Quass
CIKM
2010
Springer
15 years 3 months ago
Rank learning for factoid question answering with linguistic and semantic constraints
This work presents a general rank-learning framework for passage ranking within Question Answering (QA) systems using linguistic and semantic features. The framework enables query...
Matthew W. Bilotti, Jonathan L. Elsas, Jaime G. Ca...
ICRA
2010
IEEE
116views Robotics» more  ICRA 2010»
15 years 3 months ago
Learning visibility of landmarks for vision-based localization
— We aim to perform robust and fast vision-based localization using a pre-existing large map of the scene. A key step in localization is associating the features extracted from t...
Pablo Fernández Alcantarilla, Sang Min Oh, ...
129
Voted
ICDM
2010
IEEE
135views Data Mining» more  ICDM 2010»
15 years 3 months ago
Learning a Bi-Stochastic Data Similarity Matrix
An idealized clustering algorithm seeks to learn a cluster-adjacency matrix such that, if two data points belong to the same cluster, the corresponding entry would be 1; otherwise ...
Fei Wang, Ping Li, Arnd Christian König
TNN
2010
176views Management» more  TNN 2010»
14 years 11 months ago
Sparse approximation through boosting for learning large scale kernel machines
Abstract--Recently, sparse approximation has become a preferred method for learning large scale kernel machines. This technique attempts to represent the solution with only a subse...
Ping Sun, Xin Yao