Search Results for author:
Found 6 papers, 2 papers with code
A Step by Step Mathematical Derivation and Tutorial on Kalman Filters
We present a step by step mathematical derivation of the Kalman filter using two different approaches.
Other Statistics
Kernel Two-Sample Hypothesis Testing Using Kernel Set Classification
In the set classification problem the goal is to classify a set of data points that are assumed to have a common class.
Strictly Proper Kernel Scoring Rules and Divergences with an Application to Kernel Two-Sample Hypothesis Testing
We consider conditions under which the Kernel Score is strictly proper.
Cost-Sensitive Support Vector Machines
Minimization of the new hinge loss is shown to be a generalization of the classic SVM optimization problem, and can be solved by identical procedures.
Variable margin losses for classifier design
It is shown that, when the risk is in canonical form and the link is inverse sigmoidal, the margin properties of the loss are determined by a single parameter.
On the Design of Loss Functions for Classification: theory, robustness to outliers, and SavageBoost
This shows that the standard approach of proceeding from the specification of a loss, to the minimization of conditional risk is overly restrictive.
