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» Semi-Supervised Model Selection Based on Cross-Validation
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IJCNN
2006
IEEE
13 years 11 months ago
Semi-Supervised Model Selection Based on Cross-Validation
We propose a new semi-supervised model selection method that is derived by applying the structural risk minimization principle to a recent semi-supervised generalization error bou...
Matti Kaariainen
WACV
2005
IEEE
13 years 10 months ago
Semi-Supervised Self-Training of Object Detection Models
The construction of appearance-based object detection systems is time-consuming and difficult because a large number of training examples must be collected and manually labeled i...
Chuck Rosenberg, Martial Hebert, Henry Schneiderma...
ICMLA
2007
13 years 6 months ago
Semi-Supervised Active Learning for Modeling Medical Concepts from Free Text
We apply a new active learning formulation to the problem of learning medical concepts from unstructured text. The new formulation is based on maximizing the mutual information th...
Rómer Rosales, Praveen Krishnamurthy, R. Bh...
ICDM
2005
IEEE
185views Data Mining» more  ICDM 2005»
13 years 10 months ago
Semi-Supervised Mixture of Kernels via LPBoost Methods
We propose an algorithm to construct classification models with a mixture of kernels from labeled and unlabeled data. The derived classifier is a mixture of models, each based o...
Jinbo Bi, Glenn Fung, Murat Dundar, R. Bharat Rao
IJCNN
2006
IEEE
13 years 11 months ago
Leave-One-Out Cross-Validation Based Model Selection Criteria for Weighted LS-SVMs
Abstract— While the model parameters of many kernel learning methods are given by the solution of a convex optimisation problem, the selection of good values for the kernel and r...
Gavin C. Cawley