Research output

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2009
Published
Combining Classifiers for Improved Multilabel Image Classification
Antenreiter, M., Ortner, R. & Auer, P., 2009.
Research output: Contribution to conference › Poster › Research › peer-review
Published
Consistent Interpretation of Image Sequences to Improve Object Models on the fly
Prankl, J., Antenreiter, M., Auer, P. & Vincze, M., 2009, Computer Vision Systems. p. 384-393
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Published
Geochemisches Fingerprinting durch maschinelles Lernen
Rantitsch, G. & Savu-Krohn, C., 2009
Research output: Book/Report › Commissioned report › Transfer › peer-review
Published
Image categorisation through Boosting using cost-minimising strategies for data labelling
Savu-Krohn, C., 2009
Research output: Thesis › Doctoral Thesis
Published
Near-optimal Regret Bounds for Reinforcement Learning
Auer, P., Jaksch, T. & Ortner, R., 2009
Research output: Book/Report › Commissioned report › Transfer › peer-review
Published
Near-optimal Regret Bounds for Reinforcement Learning
Auer, P., Jaksch, T. & Ortner, R., 2009, Advances in neural information processing systems 21. MIT Press, p. 89-96
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Published
Relevance Feedback Models for Content-Based Image Retrieval
Auer, P. & Leung, P., 2009, Multimedia Analysis, Processing and Communications.
Research output: Chapter in Book/Report/Conference proceeding › Chapter › Research
Published
Using a spatio-temporal reasoning system to improve object models on the fly
Antenreiter, M., Prankl, J., Vincze, M. & Auer, P., 2009, 33rd Workshop of the Austrian Association for Pattern Recognition - Visual Learning. p. 25-36
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
2008
Published
Guest editors' introduction: Special issue on learning theory
Auer, P. & Long, P. M., 1 Dec 2008, In: Journal of Computer and System Sciences. 74, 8, 1 p.
Research output: Contribution to journal › Editorial › peer-review
Published
A learning rule for very simple universal approximators consisting of a single layer of perceptrons
Auer, P., Burgsteiner, H. & Maass, W., 2008, In: Neural networks. 21, p. 786-795
Research output: Contribution to journal › Article › Research › peer-review

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Research Portal

Research output

Combining Classifiers for Improved Multilabel Image Classification

Consistent Interpretation of Image Sequences to Improve Object Models on the fly

Geochemisches Fingerprinting durch maschinelles Lernen

Image categorisation through Boosting using cost-minimising strategies for data labelling

Near-optimal Regret Bounds for Reinforcement Learning

Near-optimal Regret Bounds for Reinforcement Learning

Relevance Feedback Models for Content-Based Image Retrieval

Using a spatio-temporal reasoning system to improve object models on the fly

Guest editors' introduction: Special issue on learning theory

A learning rule for very simple universal approximators consisting of a single layer of perceptrons

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