Prohlížení Publikační činnost - 13136 dle názvu
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Block-Wise Construction of Acyclic Relational Features with Monotone Irreducibility and Relevancy Properties
(2009)We describe an algorithm for constructing a set of acyclic conjunctive relational features by combining smaller conjunctive blocks. Unlike traditional level-wise approaches which preserve the monotonicity of frequency, our ... -
Comparative evaluation of set-level techniques in predictive classification of gene expression
(2012)Background: Analysis of gene expression data in terms of a priori-defined gene sets has recently received significant attention as this approach typically yields more compact and interpretable results than those produced by ... -
Fast Estimation of First Order Clause Coverage through Randomization and Maximum Likelihood
(2008)In inductive logic programming, µ-subsumption is a widely used coveragetest. Unfortunately, testing µ-subsumption is NP-complete, which represents a crucial efficiency bottleneck for many relational learners. In this ... -
Gaussian Logic for Predictive Classification
(2011)We describe a statistical relational learning framework called Gaussian Logic capable to work efficiently with combinations of relational and numerical data. The framework assumes that, for a fixed relational structure, ... -
Prediction of DNA-binding propensity of proteins by the ball-histogram method using automatic template search
(2012)We contribute a novel, ball-histogram approach to DNA-binding propensity prediction of proteins. Unlike state-ofthe- art methods based on constructing an ad-hoc set of features describing physicochemical properties of ... -
Prediction of DNA-binding proteins from relational features
(2012)Background: The process of protein-DNA binding has an essential role in the biological processing of genetic information. We use relational machine learning to predict DNA-binding propensity of proteins from their ... -
A Restarted Strategy for Efficient Subsumption Testing
(2008)We study runtime distributions of subsumption testing. On graph data randomly sampled from two different generative models we observe a gradual growth of the tails of the distributions as a function of the problem instance ...