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CSDA
2004
124views more  CSDA 2004»
13 years 7 months ago
Fast and robust discriminant analysis
The goal of discriminant analysis is to obtain rules that describe the separation between groups of observations. Moreover it allows to classify new observations into one of the k...
Mia Hubert, Katrien van Driessen
AAAI
2010
13 years 9 months ago
Robust Policy Computation in Reward-Uncertain MDPs Using Nondominated Policies
The precise specification of reward functions for Markov decision processes (MDPs) is often extremely difficult, motivating research into both reward elicitation and the robust so...
Kevin Regan, Craig Boutilier
CORR
2006
Springer
126views Education» more  CORR 2006»
13 years 7 months ago
Evaluating the Robustness of Learning from Implicit Feedback
This paper evaluates the robustness of learning from implicit feedback in web search. In particular, we create a model of user behavior by drawing upon user studies in laboratory ...
Filip Radlinski, Thorsten Joachims
ESANN
2003
13 years 9 months ago
Robust Topology Representing Networks
Martinetz and Schulten proposed the use of a Competitive Hebbian Learning (CHL) rule to build Topology Representing Networks. From a set of units and a data distribution, a link i...
Michaël Aupetit
BMCBI
2007
123views more  BMCBI 2007»
13 years 7 months ago
Robust clustering in high dimensional data using statistical depths
Background: Mean-based clustering algorithms such as bisecting k-means generally lack robustness. Although componentwise median is a more robust alternative, it can be a poor cent...
Yuanyuan Ding, Xin Dang, Hanxiang Peng, Dawn Wilki...