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ICML
2007
IEEE
14 years 11 months ago
Learning from interpretations: a rooted kernel for ordered hypergraphs
The paper presents a kernel for learning from ordered hypergraphs, a formalization that captures relational data as used in Inductive Logic Programming (ILP). The kernel generaliz...
Gabriel Wachman, Roni Khardon
AAAI
2010
13 years 12 months ago
Structure Learning for Markov Logic Networks with Many Descriptive Attributes
Many machine learning applications that involve relational databases incorporate first-order logic and probability. Markov Logic Networks (MLNs) are a prominent statistical relati...
Hassan Khosravi, Oliver Schulte, Tong Man, Xiaoyua...
BMCBI
2004
140views more  BMCBI 2004»
13 years 10 months ago
What can we learn from noncoding regions of similarity between genomes?
Background: In addition to known protein-coding genes, large amounts of apparently non-coding sequence are conserved between the human and mouse genomes. It seems reasonable to as...
Thomas A. Down, Tim J. P. Hubbard
TSD
2010
Springer
13 years 8 months ago
A Priori and A Posteriori Machine Learning and Nonlinear Artificial Neural Networks
The main idea of a priori machine learning is to apply a machine learning method on a machine learning problem itself. We call it "a priori" because the processed data se...
Jan Zelinka, Jan Romportl, Ludek Müller
GI
2009
Springer
14 years 3 months ago
Applying Semantic Technologies for Context-Aware AAL Services: What we can learn from SOPRANO
Abstract: Ambient assisted living (AAL) is a newly emerging term describing a research area with focus on services that support people in their daily life with particular focus on ...
Peter Wolf, Andreas Schmidt, Michael Klein