5 citations · 5 across the 2 of their papers we have counts for
4 papers · 1 filter
Probabilistic Active Learning for Active Class Selection
Daniel Kottke, Georg Krempl, Marianne Stecklina +6
In machine learning, active class selection (ACS) algorithms aim to actively select a class and ask the oracle to provide an instance for that class to optimize a classifier's perf…
Active Selection of Classification Features
Thomas T. Kok, Rachel M. Brouwer, Rene M. Mandl +2
Some data analysis applications comprise datasets, where explanatory variables are expensive or tedious to acquire, but auxiliary data are readily available and might help to const…
Toward Optimal Probabilistic Active Learning Using a Bayesian Approach
Daniel Kottke, Marek Herde, Christoph Sandrock +3
Gathering labeled data to train well-performing machine learning models is one of the critical challenges in many applications. Active learning aims at reducing the labeling costs…
Temporal Density Extrapolation using a Dynamic Basis Approach
Georg Krempl, Dominik Lang, Vera Hofer
Density estimation is a versatile technique underlying many data mining tasks and techniques,ranging from exploration and presentation of static data, to probabilistic classificati…