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PKDD
2010
Springer
160views Data Mining» more  PKDD 2010»
13 years 5 months ago
Entropy and Margin Maximization for Structured Output Learning
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...
PAMI
2010
132views more  PAMI 2010»
13 years 5 months ago
Maximum Likelihood Model Selection for 1-Norm Soft Margin SVMs with Multiple Parameters
—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....
Tobias Glasmachers, Christian Igel
NECO
2010
101views more  NECO 2010»
13 years 2 months ago
Large-Margin Classification in Infinite Neural Networks
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...
Youngmin Cho, Lawrence K. Saul
GBRPR
2007
Springer
13 years 11 months ago
Image Classification Using Marginalized Kernels for Graphs
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...
Emanuel Aldea, Jamal Atif, Isabelle Bloch
NIPS
2007
13 years 9 months ago
The Generalized FITC Approximation
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...
Andrew Naish-Guzman, Sean B. Holden