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» On Kernel Methods for Relational Learning
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KDD
2009
ACM
192views Data Mining» more  KDD 2009»
15 years 4 months ago
Primal sparse Max-margin Markov networks
Max-margin Markov networks (M3 N) have shown great promise in structured prediction and relational learning. Due to the KKT conditions, the M3 N enjoys dual sparsity. However, the...
Jun Zhu, Eric P. Xing, Bo Zhang
ISMB
2000
14 years 11 months ago
Analysis of Gene Expression Microarrays for Phenotype Classification
Several microarray technologies that monitor the level of expression of a large number of genes have recently emerged. Given DNA-microarray data for a set of cells characterized b...
Andrea Califano, Gustavo Stolovitzky, Yuhai Tu
AAAI
1994
14 years 11 months ago
Solution Reuse in Dynamic Constraint Satisfaction Problems
Many AI problems can be modeled as constraint satisfaction problems (CSP), but many of them are actually dynamic: the set of constraints to consider evolves because of the environ...
Gérard Verfaillie, Thomas Schiex
AES
2008
Springer
133views Cryptology» more  AES 2008»
14 years 9 months ago
Alternative neural networks to estimate the scour below spillways
Artificial neural networks (ANN's) are associated with difficulties like lack of success in a given problem and unpredictable level of accuracy that could be achieved. In eve...
H. Md. Azamathulla, M. C. Deo, P. B. Deolalikar
DATAMINE
2008
143views more  DATAMINE 2008»
14 years 9 months ago
Automatically countering imbalance and its empirical relationship to cost
Learning from imbalanced datasets presents a convoluted problem both from the modeling and cost standpoints. In particular, when a class is of great interest but occurs relatively...
Nitesh V. Chawla, David A. Cieslak, Lawrence O. Ha...