about this site

Welcome to my blog, where I periodically post about data science and machine learning, do-it-yourself projects and other tech related topics.

It is a mixed blog so if you are interested in a single topic you can subscribe the respective feed on the bottom of the page.

publications

October, 2018

Improving Active Learning by Avoiding Ambiguous Samples

Christian Limberg, Heiko Wersing, Helge Ritter

International Conference on Artificial Neural Networks (ICANN)

Abstract: If label information in a classification task is expensive, it can be beneficial to use active learning to get the most informative samples to label by a human. However, there can be samples which are meaningless to the human or recorded wrongly. If these samples are near the classifier’s decision boundary, they are queried repeatedly for labeling. This is inefficient for training because the human can not label these samples correctly and this may lower human acceptance. We introduce an approach to compensate the problem of ambiguous samples by excluding clustered samples from labeling. We compare this approach to other state-of-the-art methods. We further show that we can improve the accuracy in active learning and reduce the number of ambiguous samples queried while training.




April, 2018

Efficient Accuracy Estimation for Instance-Based Incremental Active Learning

Christian Limberg, Heiko Wersing, Helge Ritter

European Symposium on Artificial Neural Networks (ESANN)

Abstract: Estimating systems accuracy is crucial for applications of incremental learning. In this paper, we introduce the Distogram Estimation (DGE) approach to estimate the accuracy of instance-based classifiers. By calculating relative distances to samples it is possible to train an offline regression model, capable of predicting the classifiers accuracy on unseen data. Our approach requires only a few supervised samples for training and can instantaneously be applied on unseen data afterwards. We evaluate our method on five benchmark data sets and for a robot object recognition task. Our algorithm clearly outperforms two baseline methods both for random and active selection of incremental training examples.

projects

ALeFra – Active Learning Framework

Convert any classifier (online and offline) in an active trainable classifier. The classifier can be trained easily and the training progress can be visualized in different ways to better understand what is going on. Please check the Github page for full example usages and interactive result plots.


about me

I am a PhD-student from Frankfurt in Germany. My research topics are machine learning and human robot interaction. More precisely I work on active learning and cooperative intelligence in task completion right now.

In my free time I like to make things. Some of them are tech related, but others are not. I am also interested in programming, web-programming, robotics and drones, IoT, photography and making delicious filter coffee or espresso. Last but not least I am a big fan and collector of retro video games, especially N64 and SNES. You can take a look at my complete collection.

just me

contact

You can contact me via my email address climberg[at]techfak[d0t]uni-bielefeld[d0t]de