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Learning Kernels for Structured Prediction using Polynomial Kernel Transformations
In this work we focus on learning kernel representations for structured regression.
Supervised Dimensionality Reduction via Distance Correlation Maximization
In our work, we propose a novel formulation for supervised dimensionality reduction based on a nonlinear dependency criterion called Statistical Distance Correlation, Szekely et.
Simultaneous Twin Kernel Learning using Polynomial Transformations for Structured Prediction
We formulate this problem for learning covariances kernels of Twin Gaussian Processes.
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