CAIP
2009
Springer
14 years 6 months ago
2009
Springer
CAIP
2009
Springer
14 years 6 months ago
2009
Springer
Smoothing of biomedical images should preserve gray-level transitions between adjacent tissues, while restoring contours consistent with anatomical structures. Anisotropic diffusi...
ACIVS
2009
Springer
14 years 6 months ago
2009
Springer
Abstract. In the pattern recognition context, objects can be represented as graphs with attributed nodes and edges involving their relations. Consequently, matching attributed grap...
MCS
2009
Springer
14 years 6 months ago
2009
Springer
Abstract. One of the most widely applied techniques to deal with multiclass categorization problems is the pairwise voting procedure. Recently, this classical approach has been emb...
MCS
2009
Springer
14 years 6 months ago
2009
Springer
Abstract. Hierarchical classification problems gained increasing attention within the machine learning community, and several methods for hierarchically structured taxonomies have...
MCS
2009
Springer
14 years 6 months ago
2009
Springer
Abstract. We propose AdaBoost.BHC, a novel multi-class boosting algorithm. AdaBoost.BHC solves a C class problem by using C − 1 binary classifiers defined by a hierarchy that i...
MCS
2009
Springer
14 years 6 months ago
2009
Springer
The classification of hyperspectral imagery, using multiple classifier systems is discussed and an SVM-based ensemble is introduced. The data set is separated into separate featu...
MCS
2009
Springer
14 years 6 months ago
2009
Springer
Machine Learning can be divided into two schools of thought: generative model learning and discriminative model learning. While the MCS community has been focused mainly on the lat...
MCS
2009
Springer
14 years 6 months ago
2009
Springer
Weighted averaging of classifier outputs is used in many MCSs, yet is still not well understood. Several empirical studies have investigated the effect that non-negativity and su...
MCS
2009
Springer
14 years 6 months ago
2009
Springer
Abstract. Semi-supervised learning and ensemble learning are two important learning paradigms. The former attempts to achieve strong generalization by exploiting unlabeled data; th...