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ML
2008
ACM
146views Machine Learning» more  ML 2008»
15 years 3 months ago
Improving maximum margin matrix factorization
Abstract. Collaborative filtering is a popular method for personalizing product recommendations. Maximum Margin Matrix Factorization (MMMF) has been proposed as one successful lear...
Markus Weimer, Alexandros Karatzoglou, Alex J. Smo...
SAC
2009
ACM
15 years 10 months ago
Evaluating algorithms that learn from data streams
In the past years, the theory and practice of machine learning and data mining have been focused on static and finite data sets from where learning algorithms generate a static m...
João Gama, Pedro Pereira Rodrigues, Raquel ...
145
Voted
ECML
2005
Springer
15 years 9 months ago
Learning to Complete Sentences
Abstract. We consider the problem of predicting how a user will continue a given initial text fragment. Intuitively, our goal is to develop a “tab-complete” function for natura...
Steffen Bickel, Peter Haider, Tobias Scheffer
118
Voted
TIT
1998
70views more  TIT 1998»
15 years 3 months ago
The Importance of Convexity in Learning with Squared Loss
We show that if the closureof a function class F under the metric induced by some probability distribution is not convex, then the sample complexity for agnostically learning F wi...
Wee Sun Lee, Peter L. Bartlett, Robert C. Williams...
ICPR
2006
IEEE
16 years 4 months ago
A New Data Selection Principle for Semi-Supervised Incremental Learning
Current semi-supervised incremental learning approaches select unlabeled examples with predicted high confidence for model re-training. We show that for many applications this dat...
Alexander I. Rudnicky, Rong Zhang