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» Dimensions of machine learning in design
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ICML
2004
IEEE
14 years 8 months ago
Support vector machine learning for interdependent and structured output spaces
Learning general functional dependencies is one of the main goals in machine learning. Recent progress in kernel-based methods has focused on designing flexible and powerful input...
Ioannis Tsochantaridis, Thomas Hofmann, Thorsten J...
GCB
2003
Springer
164views Biometrics» more  GCB 2003»
14 years 28 days ago
Integrative machine learning approach for multi-class SCOP protein fold classification
: Classification and prediction of protein structure has been a central research theme in structural bioinformatics. Due to the imbalanced distribution of proteins over multi SCOP ...
Aik Choon Tan, David Gilbert, Yves Deville
IRI
2008
IEEE
14 years 2 months ago
Compound record clustering algorithm for design pattern detection by decision tree learning
Recovering design patterns applied in a system can help refactoring the system. Machine learning algorithms have been successfully applied in mining data patterns. However, one of...
Jing Dong, Yongtao Sun, Yajing Zhao
MLDM
2005
Springer
14 years 1 months ago
SSC: Statistical Subspace Clustering
Subspace clustering is an extension of traditional clustering that seeks to find clusters in different subspaces within a dataset. This is a particularly important challenge with...
Laurent Candillier, Isabelle Tellier, Fabien Torre...
ICML
2007
IEEE
14 years 8 months ago
Sample compression bounds for decision trees
We propose a formulation of the Decision Tree learning algorithm in the Compression settings and derive tight generalization error bounds. In particular, we propose Sample Compres...
Mohak Shah