Towards layer-wise training of deep Kernel networks
n recent times, there has been a growing interest in integrating kernel methods with neural networks, capitalizing on the expressive capabilities of the former and the generalization strengths of the latter. On of the aims of the study was contribute to the understanding and development of the kchai...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/403814 |
| Acceso en línea: | https://hdl.handle.net/2117/403814 |
| Access Level: | acceso abierto |
| Palabra clave: | Kernel functions Neural networks (Computer science) deep kernel learning xarxa neuronal mètode del nucli Nucli de funció de base radial neural network kernel methods RBF kernel Kernel, Funcions de Xarxes neuronals (Informàtica) Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Sumario: | n recent times, there has been a growing interest in integrating kernel methods with neural networks, capitalizing on the expressive capabilities of the former and the generalization strengths of the latter. On of the aims of the study was contribute to the understanding and development of the kchain, a recent proposal in this line. Most prominently, this thesis introduces the Kernel Activation Layer (KAL), a novel activation function designed to process input from three linear layers using a kernel function. This research offers a comprehensive examination of the gradients associ- ated with the Radial Basis Function (RBF) kernel and presents an analytical approach to selecting the optimal value for its hyperparameter, denoted as ¿. Experimental findings illustrate that models incorporating the KAL can match the performance of conventional neural networks, particularly excelling in defining clear decision bound- aries for non-linear datasets with sharp class boundaries. |
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