In this paper, we improve on the multiple kernel learning approach by successfully optimizing multiple layers each with multiple kernels. III. BACKGROUND.
Sep 18, 2015 · MKL consists of combining a set of already defined base kernels so as to learn the optimal kernel. It exhibits its strength of learning multiple ...
In this paper, we take a different approach by learning multiple layers of kernels. We combine kernels at each layer and then optimize over an estimate of ...
In this paper, we investigate a frame- work of Multi-Layer Multiple Kernel Learn- ing (MLMKL) that aims to learn “deep” ker- nel machines by exploring the ...
The proposed method, named KerNET, is experimentally evaluated on two well-known neural architectures, namely the Multi-layer Perceptron (MLP), and the ...
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Jul 14, 2014 · Deep learning refers to artificial neural networks that are composed of many layers. It's a growing trend in ML due to some favorable ...
These methods utilize a multilayer structure of deep architecture to design more complex kernels than standard MKL algorithms. ... ... In this schema, ...
This paper combines kernels at each layer and then optimize over an estimate of the support vector machine leave-one-out error rather than the dual ...
Sep 17, 2020 · It aims to learn “deep” kernel machines by exploring a combination of multiple kernels in a multi-layer structure. With multi-layer mapping, the ...
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 ...