137 search results - page 6 / 28 » Quantifying the Impact of Learning Algorithm Parameter Tunin... |
ICANN
2005
Springer
14 years 2 months ago
2005
Springer
Abstract. Tuning hyper-parameters is a necessary step to improve learning algorithm performances. For Support Vector Machine classifiers, adjusting kernel parameters increases dra...
ICASSP
2011
IEEE
13 years 10 days ago
2011
IEEE
Constrained discriminative linear transform (CDLT) optimized with Extended Baum-Welch (EBW) has been presented in the literature as a discriminative speaker adaptation method that...
SIGCOMM
1997
ACM
14 years 24 days ago
1997
ACM
— Measurement-based admission control (MBAC) is an attractive mechanism to concurrently offer quality of service (QoS) to users, without requiring a priori traffic specificatio...
EUSFLAT
2003
13 years 10 months ago
2003
In this paper, an architecture of a resourceallocating learning probabilistic neural network is considered. Construction and learning algorithms are proposed. The advantages of th...
ICML
2009
IEEE
14 years 9 months ago
2009
IEEE
We present a general Bayesian framework for hyperparameter tuning in L2-regularized supervised learning models. Paradoxically, our algorithm works by first analytically integratin...