PRL
2006
14 years 1 months ago
2006
This paper presents a general method for incorporating prior knowledge into kernel methods such as Support Vector Machines. It applies when the prior knowledge can be formalized b...
ML
2008
ACM
14 years 1 months ago
2008
ACM
This paper explores the addition of constraints to the linear programming formulation of the support vector regression problem for the incorporation of prior knowledge. Equality an...
JAIR
2006
14 years 1 months ago
2006
We present a novel framework for integrating prior knowledge into discriminative classifiers. Our framework allows discriminative classifiers such as Support Vector Machines (SVMs...
IJAR
2006
14 years 1 months ago
2006
We consider the problem of learning the parameters of a Bayesian network from data, while taking into account prior knowledge about the signs of influences between variables. Such...
IEICET
2008
14 years 1 months ago
2008
Obtaining the best linear unbiased estimator (BLUE) of noisy signals is a traditional but powerful approach to noise reduction. Explicitly computing the BLUE usually requires the ...
AUTOMATICA
2006
14 years 1 months ago
2006
Design of iterative learning control (ILC) often requires some prior knowledge about a system's control matrix. In some applications, such as uncalibrated visual servoing, th...
BMCBI
2010
14 years 1 months ago
2010
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...
ICASSP
2010
IEEE
14 years 2 months ago
2010
IEEE
ACT This works considers the problem of efficient energy allocation of resources in a continuous fashion in determining the location of targets in a sparse environment. We extend ...
ICASSP
2010
IEEE
14 years 2 months ago
2010
IEEE
Existing cover song detection systems require prior knowledge of the number of cover songs in a test set in order to identify cover(s) to a reference song. We describe a system th...
ICA
2010
Springer
14 years 2 months ago
2010
Springer
Abstract. We discuss causal structure learning based on linear structural equation models. Conventional learning methods most often assume Gaussianity and create many indistinguish...