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» Superset Learning Based on Generalized Loss Minimization
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ICML
2005
IEEE
16 years 6 months ago
Compact approximations to Bayesian predictive distributions
We provide a general framework for learning precise, compact, and fast representations of the Bayesian predictive distribution for a model. This framework is based on minimizing t...
Edward Snelson, Zoubin Ghahramani
KDD
2005
ACM
149views Data Mining» more  KDD 2005»
15 years 11 months ago
A distributed learning framework for heterogeneous data sources
We present a probabilistic model-based framework for distributed learning that takes into account privacy restrictions and is applicable to scenarios where the different sites ha...
Srujana Merugu, Joydeep Ghosh
COLT
2001
Springer
15 years 10 months ago
Limitations of Learning via Embeddings in Euclidean Half-Spaces
The notion of embedding a class of dichotomies in a class of linear half spaces is central to the support vector machines paradigm. We examine the question of determining the mini...
Shai Ben-David, Nadav Eiron, Hans-Ulrich Simon
WPES
2004
ACM
15 years 11 months ago
Assessing global disclosure risk in masked microdata
In this paper, we introduce a general framework for microdata and three disclosure risk measures (minimal, maximal and weighted). We classify the attributes from a given microdata...
Traian Marius Truta, Farshad Fotouhi, Daniel C. Ba...
WWW
2011
ACM
15 years 27 days ago
Parallel boosted regression trees for web search ranking
Gradient Boosted Regression Trees (GBRT) are the current state-of-the-art learning paradigm for machine learned websearch ranking — a domain notorious for very large data sets. ...
Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal...