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ICML
2009
IEEE
16 years 7 months ago
Learning Markov logic network structure via hypergraph lifting
Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. Learning ML...
Stanley Kok, Pedro Domingos
ICML
2003
IEEE
16 years 7 months ago
Hidden Markov Support Vector Machines
This paper presents a novel discriminative learning technique for label sequences based on a combination of the two most successful learning algorithms, Support Vector Machines an...
Yasemin Altun, Ioannis Tsochantaridis, Thomas Hofm...
ICML
2001
IEEE
16 years 7 months ago
Learning to Generate Fast Signal Processing Implementations
A single signal processing algorithm can be represented by many mathematically equivalent formulas. However, when these formulas are implemented in code and run on real machines, ...
Bryan Singer, Manuela M. Veloso
ICSE
2008
IEEE-ACM
16 years 7 months ago
Design patterns: between programming and software design
In computer science curricula the two areas programming and software engineering are usually separated. In programming students learn an object oriented language and then deepen t...
Christoph Denzler, Dominik Gruntz
SIGSOFT
2009
ACM
16 years 7 months ago
Learning from examples to improve code completion systems
The suggestions made by current IDE's code completion features are based exclusively on static type system of the programming language. As a result, often proposals are made ...
Marcel Bruch, Martin Monperrus, Mira Mezini
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