In this paper we present a method to cluster large datasets that change over time using incremental learning techniques. The approach is based on the dynamic representation of clus...
Given an adequate simulation model of the task environment and payoff function that measures the quality of partially successful plans, competition-based heuristics such as geneti...
We consider the problem of ordinal classification, in which a value set of the decision attribute (output, dependent variable) is finite and ordered. This problem shares some chara...
Krzysztof Dembczynski, Wojciech Kotlowski, Roman S...
Abstract-- Policy Gradients with Parameter-based Exploration (PGPE) is a novel model-free reinforcement learning method that alleviates the problem of high-variance gradient estima...
Frank Sehnke, Alex Graves, Christian Osendorfer, J...
Background: Multicategory Support Vector Machines (MC-SVM) are powerful classification systems with excellent performance in a variety of data classification problems. Since the p...