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Prof. Dr. Thomas Hofmann

 

Bild von Hofmann, Thomas, Prof. Dr.

Thomas Hofmann

Position: Wissenschaftler  Abteilung: 

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Bücher (1):

Bakir GH, Hofmann T, Schölkopf B, Smola AJ, Taskar B und Vishwanathan SVN: Predicting Structured Data, 360, MIT Press, Cambridge, MA, USA, (September-2007). ISBN: 0-262-02617-1, Series: Advances in neural information processing systems

Tagungsbände (1):

Schölkopf B, Platt J und Hofmann T: Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference, Twentieth Annual Conference on Neural Information Processing Systems (NIPS 2006), 1690, MIT Press, Cambridge, MA, USA, (September-2007).
0-262-19568-2

Artikel (3):

Lampert CH, Blaschko MB und Hofmann T (Dezember-2009) Efficient Subwindow Search: A Branch and Bound Framework for Object Localization IEEE Transactions on Pattern Analysis and Machine Intelligence 31(12) 2129-2142.
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Hofmann T, Schölkopf B und Smola AJ (Juni-2008) Kernel Methods in Machine Learning Annals of Statistics 36(3) 1171-1220.
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Tsochantaridis I, Joachims T, Hofmann T und Altun Y (September-2005) Large Margin Methods for Structured and Interdependent Output Variables Journal of Machine Learning Research 6 1453-1484.

Beiträge zu Tagungsbänden (13):

Lampert CH, Blaschko MB und Hofmann T (Juni-2008) Beyond Sliding Windows: Object Localization by Efficient Subwindow Search, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), IEEE Computer Society, Los Alamitos, CA, USA, 1-8.
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Blaschko MB und Hofmann T (Dezember-8-2006) Conformal Multi-Instance Kernels, NIPS 2006 Workshop on Learning to Compare Examples, 1-6.
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Wolf T, Brors B, Hofmann T und Georgii E (Dezember-2006) Global Biclustering of Microarray Data, Sixth IEEE International Conference on Data Mining (ICDMW 2006), IEEE Computer Society, Los Alamitos, CA, USA, 125-129.
Lal TN, Schröder M, Hill J, Preissl H, Hinterberger T, Mellinger J, Bogdan M, Rosenstiel W, Hofmann T, Birbaumer N und Schölkopf B (August-2005) A Brain Computer Interface with Online Feedback based on Magnetoencephalography, 22nd International Conference on Machine Learning (ICML 2005), ACM Press, New York, NY, USA, 465-472.
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Zhou D, Schölkopf B und Hofmann T (Juli-2005) Semi-supervised Learning on Directed Graphs In: Advances in Neural Information Processing Systems 17, , Eighteenth Annual Conference on Neural Information Processing Systems (NIPS 2004), MIT Press, Cambridge, MA, USA, 1633-1640.
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Altun Y, Smola AJ und Hofmann T (Juli-2004) Exponential Families for Conditional Random Fields, 20th Annual Conference on Uncertainty in Artificial Intelligence (UAI 2004), Morgan Kaufmann, San Francisco, CA, USA, 2-9.
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Altun Y, Hofmann T und Smola AJ (Juli-2004) Gaussian Process Classification for Segmenting and Annotating Sequences, Twenty-first International Conference on Machine Learning (ICML 2004), ACM Press, New York, USA, 4.
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Tsochantaridis I, Hofmann T, Joachims T und Altun Y (Juli-2004) Support vector machine learning for interdependent and structured output spaces, Twenty-first International Conference on Machine Learning (ICML 2004), ACM Press, New York, NY, USA, 104.
Basilico J und Hofmann T (Juli-2004) Unifying collaborative and content-based filtering, Twenty-first International Conference on Machine Learning (ICML 2004), ACM Press, New York, USA, 9.
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Altun Y, Hofmann T und Johnson M (Oktober-2003) Discriminative Learning for Label Sequences via Boosting In: Advances in Neural Information Processing Systems 15, , Sixteenth Annual Conference on Neural Information Processing Systems (NIPS 2002), MIT Press, Cambridge, MA, USA, 977-984.
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Altun Y und Hofmann T (September-2003) Large Margin Methods for Label Sequence Learning, 8th European Conference on Speech Communication and Technology (EuroSpeech 2003), International Speech Communication Association, Bonn, Germany, 993-996.
Altun Y, Tsochantaridis I und Hofmann T (August-2003) Hidden Markov Support Vector Machines, Twentieth International Conference on Machine Learning (ICML 2003), AAAI Press, Menlo Park, CA, USA, 3-10.
Altun Y, Johnson M und Hofmann T (Juli-2003) Loss Functions and Optimization Methods for Discriminative Learning of Label Sequences, Conference on Empirical Methods in Natural Language Processing (EMNLP 2003), ACL, East Stroudsburg, PA, USA, 145-152.

Beiträge zu Büchern (1):

Altun Y, Hofmann T und Tsochantaridis I: Support Vector Machine Learning for Interdependent and Structured Output Spaces, 85-103. In: Predicting Structured Data, (Ed) G. H. BakIr, MIT Press, Cambridge, MA, USA, (September-2007).

Technische Berichte (1):

Blaschko MB, Hofmann T und Lampert CH: Efficient Subwindow Search for Object Localization, 164, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, (August-2007).
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Last updated: Montag, 22.05.2017