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ICMLA
2009
13 years 5 months ago
Learning Deep Neural Networks for High Dimensional Output Problems
State-of-the-art pattern recognition methods have difficulty dealing with problems where the dimension of the output space is large. In this article, we propose a new framework ba...
Benjamin Labbé, Romain Hérault, Cl&e...
ICML
2005
IEEE
14 years 8 months ago
Learning class-discriminative dynamic Bayesian networks
In many domains, a Bayesian network's topological structure is not known a priori and must be inferred from data. This requires a scoring function to measure how well a propo...
John Burge, Terran Lane
ML
2006
ACM
131views Machine Learning» more  ML 2006»
13 years 7 months ago
Markov logic networks
We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge b...
Matthew Richardson, Pedro Domingos
AAAI
2004
13 years 9 months ago
Towards Autonomic Computing: Adaptive Job Routing and Scheduling
Computer systems are rapidly becoming so complex that maintaining them with human support staffs will be prohibitively expensive and inefficient. In response, visionaries have beg...
Shimon Whiteson, Peter Stone
IFSA
2007
Springer
102views Fuzzy Logic» more  IFSA 2007»
14 years 2 months ago
Strict Generalization in Multilayered Perceptron Networks
Typically the response of a multilayered perceptron (MLP) network on points which are far away from the boundary of its training data is not very reliable. When test data points ar...
Debrup Chakraborty, Nikhil R. Pal