Abstract. We consider the problem of training discriminative structured output predictors, such as conditional random fields (CRFs) and structured support vector machines (SSVMs)....
Patrick Pletscher, Cheng Soon Ong, Joachim M. Buhm...
—Adapting the hyperparameters of support vector machines (SVMs) is a challenging model selection problem, especially when flexible kernels are to be adapted and data are scarce....
We introduce a new family of positive-definite kernels for large margin classification in support vector machines (SVMs). These kernels mimic the computation in large neural netwo...
We propose in this article an image classification technique based on kernel methods and graphs. Our work explores the possibility of applying marginalized kernels to image process...
We present an efficient generalization of the sparse pseudo-input Gaussian process (SPGP) model developed by Snelson and Ghahramani [1], applying it to binary classification pro...