Recently, a successful extension of Principal Component Analysis for structured input, such as sequences, trees, and graphs, has been proposed. This allows the embedding of discret...
In structured prediction problems, outputs are not confined to binary labels; they are often complex objects such as sequences, trees, or alignments. Support Vector Machine (SVM) ...
Adaptor grammars (Johnson et al., 2007b) are a non-parametric Bayesian extension of Probabilistic Context-Free Grammars (PCFGs) which in effect learn the probabilities of entire s...
In this paper we introduce Structured Local Predictors (SLP) – A new formulation that considers the image labelling problem from a structured learning point of view. SLP are loc...
The prediction of protein secondary structure is a classical problem in bioinformatics, and in the past few years several machine learning techniques have been proposed to t. From...