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CEAS
2006
Springer
15 years 10 months ago
Learning at Low False Positive Rates
Most spam filters are configured for use at a very low falsepositive rate. Typically, the filters are trained with techniques that optimize accuracy or entropy, rather than perfor...
Wen-tau Yih, Joshua Goodman, Geoff Hulten
AUSAI
2008
Springer
15 years 8 months ago
Learning Object Representations Using Sequential Patterns
This paper explores the use of alternating sequential patterns of local features and saccading actions to learn robust and compact object representations. The temporal encoding rep...
Nobuyuki Morioka
ACL
2012
13 years 9 months ago
Discriminative Learning for Joint Template Filling
This paper presents a joint model for template filling, where the goal is to automatically specify the fields of target relations such as seminar announcements or corporate acqu...
Einat Minkov, Luke S. Zettlemoyer
SIGIR
2011
ACM
14 years 9 months ago
Active learning to maximize accuracy vs. effort in interactive information retrieval
We consider an interactive information retrieval task in which the user is interested in finding several to many relevant documents with minimal effort. Given an initial documen...
Aibo Tian, Matthew Lease
COLT
2004
Springer
16 years 5 hour ago
Regularization and Semi-supervised Learning on Large Graphs
We consider the problem of labeling a partially labeled graph. This setting may arise in a number of situations from survey sampling to information retrieval to pattern recognition...
Mikhail Belkin, Irina Matveeva, Partha Niyogi