Seminar on Internet Technologies (Summer 2016): Difference between revisions

Jump to navigation Jump to search
Line 97: Line 97:
*[http://named-data.net/wp-content/uploads/Jacob.pdf First proposal on Content Centric Networking]
*[http://named-data.net/wp-content/uploads/Jacob.pdf First proposal on Content Centric Networking]
*[http://tools.ietf.org/pdf/draft-pentikousis-icn-scenarios-04.pdf  ICN Base line scenarios]
*[http://tools.ietf.org/pdf/draft-pentikousis-icn-scenarios-04.pdf  ICN Base line scenarios]
|-
| '''Learning from Imbalanced Data''' 
When building and training classifiers for classification problems, one commonly encountered problem is that of imbalanced data. For instance, in the case of a binary classifier, this means that one class is hugely overrepresented in the data available. Training classifiers for this kind of datasets has been a problem for some time. In this work, your task is to i) precisely introduce the imbalanced data problem, ii) discuss the state of the art of approaches for mitigating this problem (both from the perspective of learning algorithms and data manipulation techniques) and iii) find out what issues still remain open until today. Note that this topic requires a background in data science, and in particular in classification algorithms. Also, this topic requires a comparatively high reading effort.
| [https://www.net.informatik.uni-goettingen.de/people/David_Koll David Koll ]
| [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5128907&tag=1]
|-
|-
|}
|}

Navigation menu