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» Information Theory, Inference, and Learning Algorithms
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86
Voted
KDD
2004
ACM
170views Data Mining» more  KDD 2004»
16 years 1 days ago
Why collective inference improves relational classification
Procedures for collective inference make simultaneous statistical judgments about the same variables for a set of related data instances. For example, collective inference could b...
David Jensen, Jennifer Neville, Brian Gallagher
119
Voted
ICML
2009
IEEE
16 years 14 days ago
Interactively optimizing information retrieval systems as a dueling bandits problem
We present an on-line learning framework tailored towards real-time learning from observed user behavior in search engines and other information retrieval systems. In particular, ...
Yisong Yue, Thorsten Joachims
90
Voted
ILP
2007
Springer
15 years 5 months ago
Bias/Variance Analysis for Relational Domains
Bias/variance analysis is a useful tool for investigating the performance of machine learning algorithms. Conventional analysis decomposes loss into errors due to aspects of the le...
Jennifer Neville, David Jensen
124
Voted
WWW
2008
ACM
16 years 10 days ago
Learning deterministic regular expressions for the inference of schemas from XML data
Inferring an appropriate DTD or XML Schema Definition (XSD) for a given collection of XML documents essentially reduces to learning deterministic regular expressions from sets of ...
Geert Jan Bex, Wouter Gelade, Frank Neven, Stijn V...
121
Voted
ICMLA
2010
14 years 9 months ago
A Probabilistic Graphical Model of Quantum Systems
Quantum systems are promising candidates of future computing and information processing devices. In a large system, information about the quantum states and processes may be incomp...
Chen-Hsiang Yeang