18 Publikationen
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2019 | Konferenzbeitrag | Angenommen | PUB-ID: 2937841Hosseini, B., & Hammer, B., Accepted. Interpretable Multiple-Kernel Prototype Learning for Discriminative Representation and Feature Selection. Presented at the The 28th ACM International Conference on Information and Knowledge Management (CIKM) , Beijing.PUB | Datei | arXiv
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2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2937839Hosseini, B., & Hammer, B., 2019. Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold. Presented at the 2019 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML), Würzburg.PUB | Datei | arXiv
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2019 | Konferenzbeitrag | PUB-ID: 2930303Hosseini, B., & Hammer, B., 2019. Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series. In M. Verleysen, ed. Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019).PUB | arXiv
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2018 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982090Hosseini, B., & Hammer, B., 2018. Non-negative Local Sparse Coding for Subspace Clustering. In W. Duivesteijn, A. Siebes, & A. Ukkonen, eds. Advances in Intelligent Data Analysis XVII. 17th International Symposium, IDA 2018, ’s-Hertogenbosch, The Netherlands, October 24–26, 2018, Proceedings. Lecture Notes in Computer Science. Cham: Springer International Publishing, pp. 137-150.PUB | DOI
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2018 | Preprint | Veröffentlicht | PUB-ID: 2921209Hosseini, B., & Hammer, B., 2018. Non-Negative Local Sparse Coding for Subspace Clustering. Advances in Intelligent Data Analysis XVII. IDA 2018.PUB | Datei | Download (ext.) | arXiv
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2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2919598Hosseini, B., & Hammer, B., 2018. Feasibility Based Large Margin Nearest Neighbor Metric Learning. In ESANN 2018. Proceedings of 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. pp. 219-224.PUB | arXiv
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2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904469Hosseini, B., et al., 2016. Non-Negative Kernel Sparse Coding for the Analysis of Motion Data. In A. E.P. Villa, P. Masulli, & A. Javier Pons Rivero, eds. Artificial Neural Networks and Machine Learning – ICANN 2016. Lecture Notes in Computer Science. no.9887 Cham: Springer, pp. 506-514.PUB | PDF | DOI | Download (ext.) | arXiv
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2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2783165Hosseini, B., & Hammer, B., 2015. Efficient Metric Learning for the Analysis of Motion Data. In 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA). Piscataway, NJ: IEEE.PUB | DOI | Download (ext.) | arXiv
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2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2914986Hosseini, B., Ahmadabadi, M.N., & Araabi, B.N., 2010. Abstract Concept Learning Approach Based on Behavioural Feature Extraction. In J. Kamaruzaman, ed. 2009 Second International Conference on Computer and Electrical Engineering. no.2 Piscataway, NJ: IEEE.PUB | PDF | DOI
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