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KDD
2009
ACM
224views Data Mining» more  KDD 2009»
14 years 1 months ago
Issues in evaluation of stream learning algorithms
Learning from data streams is a research area of increasing importance. Nowadays, several stream learning algorithms have been developed. Most of them learn decision models that c...
João Gama, Raquel Sebastião, Pedro P...
AAAI
2011
12 years 9 months ago
Incorporating Boosted Regression Trees into Ecological Latent Variable Models
Important ecological phenomena are often observed indirectly. Consequently, probabilistic latent variable models provide an important tool, because they can include explicit model...
Rebecca A. Hutchinson, Li-Ping Liu, Thomas G. Diet...
TIT
2008
90views more  TIT 2008»
13 years 9 months ago
Scanning and Sequential Decision Making for Multidimensional Data - Part II: The Noisy Case
We consider the problem of sequential decision making for random fields corrupted by noise. In this scenario, the decision maker observes a noisy version of the data, yet judged wi...
Asaf Cohen, Tsachy Weissman, Neri Merhav
BMCBI
2008
139views more  BMCBI 2008»
13 years 9 months ago
The C1C2: A framework for simultaneous model selection and assessment
Background: There has been recent concern regarding the inability of predictive modeling approaches to generalize to new data. Some of the problems can be attributed to improper m...
Martin Eklund, Ola Spjuth, Jarl E. S. Wikberg
UAI
2003
13 years 10 months ago
Preference-based Graphic Models for Collaborative Filtering
Collaborative filtering is a very useful general technique for exploiting the preference patterns of a group of users to predict the utility of items to a particular user. Previou...
Rong Jin, Luo Si, ChengXiang Zhai