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ICPR
2006
IEEE
16 years 2 months ago
On Kernel Selection in Relevance Vector Machines Using Stability Principle
In this paper we propose an alternative interpretation of Bayesian learning based on maximal evidence principle. We establish a notion of local evidence which can be viewed as a c...
Dmitry Kropotov, Nikita Ptashko, Oleg Vasiliev, Dm...
CORR
2004
Springer
151views Education» more  CORR 2004»
15 years 1 months ago
Finite-Tree Analysis for Constraint Logic-Based Languages: The Complete Unabridged Version
Logic languages based on the theory of rational, possibly infinite, trees have much appeal in that rational trees allow for faster unification (due to the safe omission of the occ...
Roberto Bagnara, Roberta Gori, Patricia M. Hill, E...
LREC
2008
128views Education» more  LREC 2008»
15 years 2 months ago
Relation between Agreement Measures on Human Labeling and Machine Learning Performance: Results from an Art History Domain
We discuss factors that affect human agreement on a semantic labeling task in the art history domain, based on the results of four experiments where we varied the number of labels...
Rebecca J. Passonneau, Thomas Lippincott, Tae Yano...
HICSS
2006
IEEE
163views Biometrics» more  HICSS 2006»
15 years 7 months ago
Learning Ranking vs. Modeling Relevance
The classical (ad hoc) document retrieval problem has been traditionally approached through ranking according to heuristically developed functions (such as tf.idf or bm25) or gene...
Dmitri Roussinov, Weiguo Fan
FLAIRS
2007
15 years 3 months ago
Context-Sensitive MTL Networks for Machine Lifelong Learning
Context-sensitive Multiple Task Learning, or csMTL, is presented as a method of inductive transfer that uses a single output neural network and additional contextual inputs for le...
Daniel L. Silver, Ryan Poirier