Other publication
Learning Preference Relations with Kronecker Kernels: Some Theoretical and Algorithmic results
Authors: Pahikkala T
Publication year: 2012
Abstract
In this talk, we consider a framework for learning various types of preference relations that is based on Kronecker product kernels and their modifications. As case studies, we consider tasks of inferring rankings of objects and learning to predict nonlinear preferences, as well as extensions to more complex preference learning problems. Next, we present theorems about the universal approximation properties of the considered kernel functions. Finally, we present computationally efficient learning algorithms for the considered problems and practical results on several application domains.
In this talk, we consider a framework for learning various types of preference relations that is based on Kronecker product kernels and their modifications. As case studies, we consider tasks of inferring rankings of objects and learning to predict nonlinear preferences, as well as extensions to more complex preference learning problems. Next, we present theorems about the universal approximation properties of the considered kernel functions. Finally, we present computationally efficient learning algorithms for the considered problems and practical results on several application domains.
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