The standard SVM formulation for binary classification is based on the Hinge loss function, where errors are considered not correlated. Due to this, local information in the featu...
Starting with a similarity function between objects, it is possible to define a distance metric (the kernel distance) on pairs of objects, and more generally on probability distr...
Sarang C. Joshi, Raj Varma Kommaraju, Jeff M. Phil...
The central issue in representing graphstructured data instances in learning algorithms is designing features which are invariant to permuting the numbering of the vertices. We pr...
We introduce a new perceptron-based discriminative learning algorithm for labeling structured data such as sequences, trees, and graphs. Since it is fully kernelized and uses poin...
This paper presents a stagewise least square (SLS) loss function for classification. It uses a least square form within each stage to approximate a bounded monotonic nonconvex los...