Wednesday, May 13th 2015
Title: Clustering of DNA sequences (ca 20 min.
Novel sequencing technologies allow for the analysis of largeamounts of genomic information. Especially, the de novo analysis ofunknown species plays an increasingly important role. Here, analysiscannot rely on existing information such as from data bases.Instead, the complex data sets require for unsupervised techniquessuch as dimension reduction and clustering. Especially, the latterplays a particular role, for example in the discovery of new lifeforms or the study of the human microbiome. We give an overview onthe problem and show how unsupervised machine learning techniques,especially clustering, can be used to enable the analysis of thishigh-dimensional and potentially noisy data.
Colloquium coordinators are Prof.dr. M. Aiello (e-mail :
Prof.dr. M. Biehl (e-mail:
Title: Visualization of Classifiers (ca. 20 min. + discussion)
Albeit automated classifiers offer a standard tool in many application areas, there exists hardly a generic possibility to directly inspect their behavior. We present a framework how to visualize a given classifier and its behavior as concerns a given data set in two dimensions. More specifically, we use modern nonlinear dimensionality reduction (DR) techniques to project a given set of data points and their relation to the classification decision boundaries. With a given data set, this framework can be used to visualize any trained classifier which provides a probability or certainty of the classification together with the predicted class label.
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