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» Learning from Ambiguously Labeled Examples
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CIKM
2009
Springer
14 years 2 months ago
Ensembles in adversarial classification for spam
The standard method for combating spam, either in email or on the web, is to train a classifier on manually labeled instances. As the spammers change their tactics, the performanc...
Deepak Chinavle, Pranam Kolari, Tim Oates, Tim Fin...
ICDM
2010
IEEE
146views Data Mining» more  ICDM 2010»
13 years 9 months ago
One-Class Matrix Completion with Low-Density Factorizations
Consider a typical recommendation problem. A company has historical records of products sold to a large customer base. These records may be compactly represented as a sparse custom...
Vikas Sindhwani, Serhat Selcuk Bucak, Jianying Hu,...
RAID
1999
Springer
14 years 3 months ago
IDS Standards: Lessons Learned to Date
: I will discuss two efforts to get Intrusion Detection Systems to work together - the Common Intrusion Detection Framework (CIDF), and the IETF's working group to develop an ...
Stuart Staniford-Chen
ICDM
2007
IEEE
187views Data Mining» more  ICDM 2007»
14 years 5 months ago
A Comparative Study of Methods for Transductive Transfer Learning
The problem of transfer learning, where information gained in one learning task is used to improve performance in another related task, is an important new area of research. While...
Andrew Arnold, Ramesh Nallapati, William W. Cohen
ICTAI
2006
IEEE
14 years 5 months ago
MI-Winnow: A New Multiple-Instance Learning Algorithm
We present MI-Winnow, a new multiple-instance learning (MIL) algorithm that provides a new technique to convert MIL data into standard supervised data. In MIL each example is a co...
Sharath R. Cholleti, Sally A. Goldman, Rouhollah R...