Titlebook: Machine Learning and Data Mining in Pattern Recognition; 7th International Co Petra Perner Conference proceedings 2011 Springer-Verlag GmbH

薄荷醇

ACE-Cost: Acquisition Cost Efficient Classifier by Hybrid Decision Tree with Local SVM Leaveses share overlapping acquisition procedures, hence the cost of acquiring them as a group is less than the sum of the individual acquisition costs. Our experiments on the standard UCI datasets, a network flow detection application, as well as on synthetic datasets show that, the proposed approach ach

圓木可阻礙

Informative Variables Selection for Multi-relational Supervised Learning is equivalent to estimate the conditional density of the target variable given the input variable in non target table. Preliminary experiments on artificial and real data sets show that the approach allows to potentially identify relevant one-to-many variables. In this article, we focus on binary v

萬花筒

Spherical Nearest Neighbor Classification: Application to Hyperspectral Datad metrics yields better classification accuracies especially for difficult tasks in spaces with complex irregular class boundaries. This promising outcome serves as a motivation for further development of new models to analyze hyperspectral images in spherical manifolds.

Medley

Quadratically Constrained Maximum a Posteriori Estimation for Binary Classifierestimate the posteriori probability; instead we construct a discriminant function that provides the same result. The criterion consists of the maximization of an expected cost function and a quadratic constraint of the discriminant function with a weighting function. By selecting different weighting

人類學(xué)家

卡死偷電

APNEA

骯臟

ACE-Cost: Acquisition Cost Efficient Classifier by Hybrid Decision Tree with Local SVM Leavesith the mere act of acquisition of a feature, e.g. CPU time needed to compute the feature out of raw data, the dollar amount spent for gleaning more information, or the patient wellness sacrificed by an invasive medical test, etc. In such applications, a budget constrains the classification process

CESS

Informative Variables Selection for Multi-relational Supervised Learningl records in secondary tables in one-to-many relationship. To cope with this one-to-many setting, most of the existing approaches try to transform the multi-table representation, namely by propositionalisation, thereby losing the naturally compact initial representation and eventually introducing st

leniency

Separability of Split Value Criterion with Weighted Separation Gainsterion. Here, the new formulation of the SSV criterion is presented and examined. The results obtained for 21 different benchmark datasets are presented and discussed in comparison with the most popular decision tree node splitting criteria like information gain and Gini index. Because the new SSV d