Sciweavers

499 search results - page 49 / 100
» Search Engines that Learn from Implicit Feedback
Sort
View
CHI
2008
ACM
15 years 11 months ago
CueFlik: interactive concept learning in image search
Web image search is difficult in part because a handful of keywords are generally insufficient for characterizing the visual properties of an image. Popular engines have begun to ...
James Fogarty, Desney S. Tan, Ashish Kapoor, Simon...
JCDL
2009
ACM
102views Education» more  JCDL 2009»
15 years 5 months ago
Unsupervised creation of small world networks for the preservation of digital objects
The prevailing model for digital preservation is that archives should be similar to a “fortress”: a large, protective infrastructure built to defend a relatively small collect...
Charles L. Cartledge, Michael L. Nelson
KDD
2009
ACM
153views Data Mining» more  KDD 2009»
15 years 11 months ago
Predicting bounce rates in sponsored search advertisements
This paper explores an important and relatively unstudied quality measure of a sponsored search advertisement: bounce rate. The bounce rate of an ad can be informally defined as t...
D. Sculley, Robert G. Malkin, Sugato Basu, Roberto...
ISMIS
2005
Springer
15 years 4 months ago
Identifying Content Blocks from Web Documents
Intelligent information processing systems, such as digital libraries or search engines index web-pages according to their informative content. However, web-pages contain several n...
Sandip Debnath, Prasenjit Mitra, C. Lee Giles
CORR
2008
Springer
107views Education» more  CORR 2008»
14 years 11 months ago
A Spectral Algorithm for Learning Hidden Markov Models
Hidden Markov Models (HMMs) are one of the most fundamental and widely used statistical tools for modeling discrete time series. In general, learning HMMs from data is computation...
Daniel Hsu, Sham M. Kakade, Tong Zhang