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» Bottom-up learning of Markov logic network structure
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PRIB
2010
Springer
192views Bioinformatics» more  PRIB 2010»
14 years 7 months ago
Structured Output Prediction of Anti-cancer Drug Activity
We present a structured output prediction approach for classifying potential anti-cancer drugs. Our QSAR model takes as input a description of a molecule and predicts the activity...
Hongyu Su, Markus Heinonen, Juho Rousu
IJCAI
2007
14 years 11 months ago
Simple Training of Dependency Parsers via Structured Boosting
Recently, significant progress has been made on learning structured predictors via coordinated training algorithms such as conditional random fields and maximum margin Markov ne...
Qin Iris Wang, Dekang Lin, Dale Schuurmans
APIN
2010
107views more  APIN 2010»
14 years 9 months ago
Extracting reduced logic programs from artificial neural networks
Artificial neural networks can be trained to perform excellently in many application areas. While they can learn from raw data to solve sophisticated recognition and analysis prob...
Jens Lehmann, Sebastian Bader, Pascal Hitzler
ML
2006
ACM
122views Machine Learning» more  ML 2006»
14 years 9 months ago
PRL: A probabilistic relational language
In this paper, we describe the syntax and semantics for a probabilistic relational language (PRL). PRL is a recasting of recent work in Probabilistic Relational Models (PRMs) into ...
Lise Getoor, John Grant
JETAI
1998
110views more  JETAI 1998»
14 years 9 months ago
Independency relationships and learning algorithms for singly connected networks
Graphical structures such as Bayesian networks or Markov networks are very useful tools for representing irrelevance or independency relationships, and they may be used to e cientl...
Luis M. de Campos