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Data Mining In a Warehouse Inventory
Keywords object recognition, signal processing, feature selection, unsupervised clustering, large scale many class classification, data mining.  +
Level Master  +
OneLineSummary A study of feature selection and distance measures for clustering big number of categories (>1000) and novelty detection in warehouse environment.  +
Prerequisites Programming skills, Machine Learning, Computer Vision, Data Mining.  +
References Zeynep Akata, Florent Perronnin, Zaid HarcZeynep Akata, Florent Perronnin, Zaid Harchaoui, Cordelia Schmid. Good Practice in Large-Scale Learning for Image Classi cation. IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2014, 36 (3), pp.507-520.<10.1109/TPAMI.2013.146>.<hal-00835810> Florent Perronnin, Zeynep Akata, Zaid Harchaoui, Cordelia Schmid. Towards Good Practice in Large-Scale Learning for Image Classification. CVPR 2012 - IEEE Computer Vision and Pattern Recognition, Jun 2012, Providence (RI), United States. IEEE, pp.3482-3489, 2012,<10.1109/CVPR.2012.6248090>.<hal-00690014> Raphael Puget, Nicolas Baskiotis, Patrick Gallinari. Sequential Dynamic Classi cation for Large Scale Multi-class Problems. Extreme Classi cation Workshop at ICML, Jul 2015, Lille,France. 2015.<hal-01207428>, Lille,France. 2015.<hal-01207428>
StudentProjectStatus Open  +
Supervisors Björn Åstrand +
TimeFrame October 2017 to June 2018, with possible extension to September 2018  +
Title Data Mining In a Warehouse Inventory  +
Categories StudentProject  +
Modification dateThis property is a special property in this wiki. 22 September 2017 14:24:11  +
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