97,576 research outputs found
Joshua Davis: Author of Spare Parts
Full text linkCitation: K-State First (2016). Joshua Davis: Author of Spare Parts [Flier]. Manhattan, Kansas: K-State First.Flyer advertising Joshua Davis's author talk at Kansas State University
Steven Johnson Author Talk Poster
No full textK-State Book NetworkA poster advertising an author talk by Steven Johnson at Kansas State University on September 3, 2014. Steven Johnson's book "The Ghost Map" was the 2014-2015 common book
Advances in artificial neural networks, machine learning and computational intelligence
Full text linkA general framework for dimensionality reduction for large data sets
No full textHammer B, Biehl M, Bunte K, Mokbel B. A general framework for dimensionality reduction for large data sets. In: WSOM'11. 2011
Nonlinear discriminative data visualization
Full text linkBunte K, Biehl M, Hammer B. Nonlinear discriminative data visualization. In: Verleysen M, ed. European Symposium on Artificial Neural Networks. Evere: d-side publications; 2009: 65-70
Advances in artificial neural networks, machine learning and computational intelligence Selected papers from the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018)
Full text linkSupervised dimension reduction mappings
No full textBunte K, Biehl M, Hammer B. Supervised dimension reduction mappings. In: Verleysen M, ed. European Symposium on Artificial Neural Networks. D side; 2011: pp. 281-286
Discriminative Visualization by Limited Rank Matrix Learning
No full textBunte K, Schneider P, Hammer B, Schleif F-M, Villmann T, Biehl M. Discriminative Visualization by Limited Rank Matrix Learning. Machine Learning Reports. Leipzig: Universität Leipzig; 2008
Relevance-based Interactive Optimization of Sonification
Full text linkHermann T, Bunte K, Ritter H. Relevance-based Interactive Optimization of Sonification. In: Martens WL, ed. Proceedings of the 13th International Conference on Auditory Display. ICAD; 2007: 461-467.This paper presents a novel approach for the interactive optimization of sonification parameters. In a closed loop, the system automatically generates modified versions of an initial (or previously selected) sonification via gradient ascend or evolutionary algorithms. The human listener directs the optimization process by providing relevance feedback about the perceptual quality of these propositions. In summary, the scheme allows users to bring in their perceptual capabilities without burdening them with computational tasks. It also allows for continuous update of exploration goals in the course of an exploration task. Finally, Interactive Optimization is a promising novel paradigm for solving the mapping problems and for a user-centred design of auditory display. The paper gives a full account on the technique, and demonstrates the optimization at hand of synthetic and real-world data sets
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