In this paper, we study classifier combination using the kernelized eigenclassifiers The eigenclassifier method, tries to handle the linear correlations among ...

In this paper, we study classifier combination using the kernelized eigenclassifiers The eigenclassifier method, tries to handle the linear correlations ...

In this paper, we study classifier combination using the kernelized eigenclassifiers The eigenclassifier method, tries to handle the linear correlations among ...

Classifier combination allows fusion of different classifiers trained on different modalities, for example visual and audio based classifiers can be combined ...

Using rules to combine the outputs of different classifiers is the basic structure of classifier combination. Fusing models from different kernel machine ...

Using rules to combine the outputs of different classifiers is the basic structure of classifier combination. Fusing models from different kernel machine ...

Abstract. Combining classifiers is to join the strengths of different classifiers to improve the classification performance. Using rules to combine the ...

Using rules to combine the outputs of different classifiers is the basic structure of classifier combination. Fusing models from different kernel machine ...

• The perceptron algorithm always choses weights that are a linear combination of D's feature vectors w = n. C k=1 sk f(xk). If the learner got example (xk ...

Abstract—We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes ...