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KDD
2006
ACM
156views Data Mining» more  KDD 2006»
16 years 7 months ago
Unsupervised learning on k-partite graphs
Various data mining applications involve data objects of multiple types that are related to each other, which can be naturally formulated as a k-partite graph. However, the resear...
Bo Long, Xiaoyun Wu, Zhongfei (Mark) Zhang, Philip...
ISRR
2005
Springer
149views Robotics» more  ISRR 2005»
16 years 4 days ago
Emergence, Exploration and Learning of Embodied Behavior
A novel model for dynamic emergence and adaptation of embodied behavior is proposed. A musculo-skeletal system is controlled by a number of chaotic elements, each of which driving...
Yasuo Kuniyoshi, Shinsuke Suzuki, Shinji Sangawa
SDM
2008
SIAM
138views Data Mining» more  SDM 2008»
15 years 8 months ago
Learning Markov Network Structure using Few Independence Tests
In this paper we present the Dynamic Grow-Shrink Inference-based Markov network learning algorithm (abbreviated DGSIMN), which improves on GSIMN, the state-ofthe-art algorithm for...
Parichey Gandhi, Facundo Bromberg, Dimitris Margar...
BMCBI
2008
165views more  BMCBI 2008»
15 years 6 months ago
Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics
Background: Mass spectrometry is a key technique in proteomics and can be used to analyze complex samples quickly. One key problem with the mass spectrometric analysis of peptides...
Wiebke Timm, Alexandra Scherbart, Sebastian Bö...
BMCBI
2010
154views more  BMCBI 2010»
15 years 6 months ago
Candidate gene prioritization by network analysis of differential expression using machine learning approaches
Background: Discovering novel disease genes is still challenging for diseases for which no prior knowledge - such as known disease genes or disease-related pathways - is available...
Daniela Nitsch, Joana P. Gonçalves, Fabian ...