scholar.google.com › citations
MKL is able to combine many kernel functions into an optimal kernel function for classification. In the process of combination, the optimal weight coefficients are found to improve the classification results.
Abstract: Combining information from various image descriptors has become a standard technique for image classification tasks. Multiple kernel learning ...
Abstract: Combining information from various image descriptors has become a standard technique for image classification tasks. Multiple kernel learning ...
Apr 12, 2016 · Multiple Kernel Learning (MKL) framework has been developed for learning an optimal combination of descriptors for object categorization.
People also ask
What is multiple kernel learning technique?
Can you run multiple kernels?
Multiple kernel learning (MKL) is a principled approach for selecting and combining kernels for a given recognition task. A number of studies have shown ...
Missing: Classification | Show results with:Classification
Feb 28, 2012 · Some good examples of feature selection and object classification on 2D image datasets, where MKL appears to perform well. Note a quick web ...
Multiple kernel learning refers to a set of machine learning methods that use a predefined set of kernels and learn an optimal linear or non-linear combination ...
By following a multiple kernel learning (MKL) approach, we automatically obtain a sparse dependency graph of relevant object classes on which to base the ...
We propose an Incremental Multiple Kernel Learning (IMKL) approach to object recognition that initializes on a generic training database and then tunes itself ...
Our objective in this paper is object category detection: the task of determining if one or more instances of a cat- egory are present in an image and, if they ...
