Skip to main content
Fig. 2 | EURASIP Journal on Image and Video Processing

Fig. 2

From: A feature fusion based localized multiple kernel learning system for real world image classification

Fig. 2

This figure illustrates different approaches of using the kernel in combination with SVM. a When data samples from different classes are not linearly separable, they are mapped from input space to higher even infinite dimension Hilbert space. In the mapped space, data samples are linearly classified by SVM. We should note that this mapping is done implicitly by introducing kernel function. b In MKL framework, multiple kernels are used instead of a single one. Fixed weights for kernels are computed in the training phase and the weighted sum of kernels is computed. c Local weights are computed for kernels in the training phase in LMKL framework. Despite MKL, they are not fixed. d Data samples are represented by heterogeneous features instead of a single one in the feature fusion based LMKL. As shown in d, data samples are represented by two features. Three kernels are computed for the feature shown in the top rectangle, and two kernels are computed for the one in the bottom rectangle

Back to article page