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Institut für Neuroinformatik

Optimization of Adaptive Systems

Group Leader

Members

The optimization of adaptive systems workgroup is concerned with the design and analysis of adaptive information processing systems. We are interested in systems that improve over time through learning, self-adaptation, and evolution. Our systems improve autonomously based on data, in contrast to manual instruction or programming.

Parameter tuning for a support vector machine

Currently we are working on

fast training and model selection for support vector machines
learning in deep networks
development of new evolutionary search algorithms and their analysis
open source implementation of a large number of machine learning algorithms

We offer theoretical and practical advice in machine learning and computational intelligence to other research groups and industrial partners.

Software: Shark

We provide and maintain a fast, modular, open source C++ library for the design and optimization of adaptive systems. It provides a diverse set of machine learning algorithms as well as methods for non-linear optimization. The Shark library works under SunOS/Linux, Mac OS X, and Windows. Shark can be downloaded from shark-ml.org.

Former Group Members

Black Box Optimization Competition (BBComp)

Dual Training of Non-linear Support-Vector-Machines on a Budget

Glasmachers, T. (2017). A Fast Incremental BSP Tree Archive for Non-dominated Points. In Evolutionary Multi-Criterion Optimization (EMO). Springer.

@inproceedings{Glasmachers2017,
	author		=	{Glasmachers, T.},
	title		=	{A Fast Incremental BSP Tree Archive for Non-dominated Points},
	booktitle	=	{Evolutionary Multi-Criterion Optimization (EMO)},
	publisher	=	{Springer},
	year		=	{2017},
}

Irmer, T., Glasmachers, T., & Maji, S. (2017). Texture attribute synthesis and transfer using feed-forward CNNs. In Winter Conference on Applications of Computer Vision (WACV). IEEE.

@inproceedings{IrmerGlasmachersMaji2017,
	author		=	{Irmer, T. and Glasmachers, T. and Maji, S.},
	title		=	{Texture attribute synthesis and transfer using feed-forward CNNs},
	booktitle	=	{Winter Conference on Applications of Computer Vision (WACV)},
	publisher	=	{IEEE},
	year		=	{2017},
}

Krause, O., Glasmachers, T., & Igel, C. (2017). Qualitative and Quantitative Assessment of Step Size Adaptation Rules. In Conference on Foundations of Genetic Algorithms (FOGA). ACM.

@inproceedings{KrauseGlasmachersIgel2017,
	author		=	{Krause, O. and Glasmachers, T. and Igel, C.},
	title		=	{Qualitative and Quantitative Assessment of Step Size Adaptation Rules},
	booktitle	=	{Conference on Foundations of Genetic Algorithms (FOGA)},
	publisher	=	{ACM},
	year		=	{2017},
}

Doğan, Ü., Glasmachers, T., & Igel, C. (2016). A Unified View on Multi-class Support Vector Classification. Journal of Machine Learning Research, 17(45), 1–32.

@article{DoğanGlasmachersIgel2016,
	author		=	{Doğan, Ü. and Glasmachers, T. and Igel, C.},
	title		=	{A Unified View on Multi-class Support Vector Classification},
	journal		=	{Journal of Machine Learning Research},
	volume		=	{17},
	number		=	{45},
	pages		=	{1–32},
	year		=	{2016},
}

Glasmachers, T. (2016). Finite Sum Acceleration vs. Adaptive Learning Rates for the Training of Kernel Machines on a Budget. In NIPS workshop on Optimization for Machine Learning.

@inproceedings{Glasmachers2016,
	author		=	{Glasmachers, T.},
	title		=	{Finite Sum Acceleration vs. Adaptive Learning Rates for the Training of Kernel Machines on a Budget},
	booktitle	=	{NIPS workshop on Optimization for Machine Learning},
	year		=	{2016},
}

Glasmachers, T. (2016). Small Stochastic Average Gradient Steps. In NIPS workshop on Optimizing the Optimizers.

@inproceedings{Glasmachers2016b,
	author		=	{Glasmachers, T.},
	title		=	{Small Stochastic Average Gradient Steps},
	booktitle	=	{NIPS workshop on Optimizing the Optimizers},
	year		=	{2016},
}

Horn, D., Demirciğlu, A., Bischl, B., Glasmachers, T., & Weihs, C. (2016). A Comparative Study on Large Scale Kernelized Support Vector Machines. Advances in Data Analysis and Classification (ADAC), 1–17.

@article{HornDemirciğluBischlEtAl2016,
	author		=	{Horn, D. and Demirciğlu, A. and Bischl, B. and Glasmachers, T. and Weihs, C.},
	title		=	{A Comparative Study on Large Scale Kernelized Support Vector Machines},
	journal		=	{Advances in Data Analysis and Classification (ADAC)},
	pages		=	{1–17},
	year		=	{2016},
}

Krause, O., Glasmachers, T., Hansen, N., & Igel, C. (2016). Unbounded Population MO-CMA-ES for the Bi-Objective BBOB Test Suite. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO).

@inproceedings{KrauseGlasmachersHansenEtAl2016,
	author		=	{Krause, O. and Glasmachers, T. and Hansen, N. and Igel, C.},
	title		=	{Unbounded Population MO-CMA-ES for the Bi-Objective BBOB Test Suite},
	booktitle	=	{Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)},
	year		=	{2016},
}

Krause, O., Glasmachers, T., & Igel, C. (2016). Multi-objective Optimization with Unbounded Solution Sets. In NIPS workshop on Bayesian Optimization.

@inproceedings{KrauseGlasmachersIgel2016,
	author		=	{Krause, O. and Glasmachers, T. and Igel, C.},
	title		=	{Multi-objective Optimization with Unbounded Solution Sets},
	booktitle	=	{NIPS workshop on Bayesian Optimization},
	year		=	{2016},
}

Loshchilov, I., & Glasmachers, T.. (2016). Anytime Bi-Objective Optimization with a Hybrid Multi-Objective CMA-ES (HMO-CMA-ES). In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO).

@inproceedings{LoshchilovGlasmachers2016,
	author		=	{Loshchilov, I. and Glasmachers, T.},
	title		=	{Anytime Bi-Objective Optimization with a Hybrid Multi-Objective CMA-ES (HMO-CMA-ES)},
	booktitle	=	{Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)},
	year		=	{2016},
}

Weihs, C., & Glasmachers, T.. (2016). Supervised Classification. In C. Weihs, Jannach, D., Vatolkin, I., & Rudolph, G. (Eds.), Music Data Analysis: Foundations and Applications.

@incollection{WeihsGlasmachers2016,
	author		=	{Weihs, C. and Glasmachers, T.},
	title		=	{Supervised Classification},
	booktitle	=	{Music Data Analysis: Foundations and Applications},
	editor		=	{Weihs, C. and Jannach, D. and Vatolkin, I. and Rudolph, G.},
	year		=	{2016},
}

Krause, O., & Glasmachers, T.. (2015). A CMA-ES with Multiplicative Covariance Matrix Updates. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO).

@inproceedings{KrauseGlasmachers2015,
	author		=	{Krause, O. and Glasmachers, T.},
	title		=	{A CMA-ES with Multiplicative Covariance Matrix Updates},
	booktitle	=	{Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)},
	year		=	{2015},
}

Danafar, S., Rancoita, P. M. V., Glasmachers, T., Whittingstall, K., & Schmidhuber, J. (2014). Testing Hypotheses by Regularized Maximum Mean Discrepancy. International Journal of Computer and Information Technology (IJCIT), 3(2).

@article{DanafarRancoitaGlasmachersEtAl2014,
	author		=	{Danafar, S. and Rancoita, P.M.V. and Glasmachers, T. and Whittingstall, K. and Schmidhuber, J.},
	title		=	{Testing Hypotheses by Regularized Maximum Mean Discrepancy},
	journal		=	{International Journal of Computer and Information Technology (IJCIT)},
	volume		=	{3},
	number		=	{2},
	year		=	{2014},
}

Glasmachers, T. (2014). Handling Sharp Ridges with Local Supremum Transformations. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO).

@inproceedings{Glasmachers2014,
	author		=	{Glasmachers, T.},
	title		=	{Handling Sharp Ridges with Local Supremum Transformations},
	booktitle	=	{Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)},
	year		=	{2014},
}

Glasmachers, T. (2014). Optimized Approximation Sets for Low-dimensional Benchmark Pareto Fronts. In Parallel Problem Solving from Nature (PPSN). Springer.

@inproceedings{Glasmachers2014b,
	author		=	{Glasmachers, T.},
	title		=	{Optimized Approximation Sets for Low-dimensional Benchmark Pareto Fronts},
	booktitle	=	{Parallel Problem Solving from Nature (PPSN)},
	publisher	=	{Springer},
	year		=	{2014},
}

Glasmachers, T., Naujoks, B., & Rudolph, G. (2014). Start Small, Grow Big - Saving Multiobjective Function Evaluations. In Parallel Problem Solving from Nature (PPSN). Springer.

@inproceedings{GlasmachersNaujoksRudolph2014,
	author		=	{Glasmachers, T. and Naujoks, B. and Rudolph, G.},
	title		=	{Start Small, Grow Big - Saving Multiobjective Function Evaluations},
	booktitle	=	{Parallel Problem Solving from Nature (PPSN)},
	publisher	=	{Springer},
	year		=	{2014},
}

Wierstra, D., Schaul, T., Glasmachers, T., Sun, Y., Peters, J., & Schmidhuber, J. (2014). Natural Evolution Strategies. Journal of Machine Learning Research, 15, 949–980.

@article{WierstraSchaulGlasmachersEtAl2014,
	author		=	{Wierstra, D. and Schaul, T. and Glasmachers, T. and Sun, Y. and Peters, J. and Schmidhuber, J.},
	title		=	{Natural Evolution Strategies},
	journal		=	{Journal of Machine Learning Research},
	volume		=	{15},
	pages		=	{949–980},
	year		=	{2014},
}

Glasmachers, T. (2013). A Natural Evolution Strategy with Asynchronous Strategy Updates. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO).

@inproceedings{Glasmachers2013,
	author		=	{Glasmachers, T.},
	title		=	{A Natural Evolution Strategy with Asynchronous Strategy Updates},
	booktitle	=	{Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)},
	year		=	{2013},
}

Glasmachers, T. (2013). The Planning-ahead SMO Algorithm (No. arxiv:1305.0423v1). arxiv.org.

@techreport{Glasmachers2013b,
	author		=	{Glasmachers, T.},
	title		=	{The Planning-ahead SMO Algorithm},
	institution	=	{arxiv.org},
	number		=	{arxiv:1305.0423v1},
	year		=	{2013},
}

Glasmachers, T., & Doğan, Ü. (2013). Accelerated Coordinate Descent with Adaptive Coordinate Frequencies. In Proceedings of the fifth Asian Conference on Machine Learning (ACML).

@inproceedings{GlasmachersDoğan2013,
	author		=	{Glasmachers, T. and Doğan, Ü.},
	title		=	{Accelerated Coordinate Descent with Adaptive Coordinate Frequencies},
	booktitle	=	{Proceedings of the fifth Asian Conference on Machine Learning (ACML)},
	year		=	{2013},
}

Krause, O., Fischer, A., Glasmachers, T., & Igel, C. (2013). Approximation properties of DBNs with binary hidden units and real-valued visible units. In Proceedings of the International Conference on Machine Learning (ICML).

@inproceedings{KrauseFischerGlasmachersEtAl2013,
	author		=	{Krause, O. and Fischer, A. and Glasmachers, T. and Igel, C.},
	title		=	{Approximation properties of DBNs with binary hidden units and real-valued visible units},
	booktitle	=	{Proceedings of the International Conference on Machine Learning (ICML)},
	year		=	{2013},
}

Doğan, Ü., Glasmachers, T., & Igel, C. (2012). Turning Binary Large-margin Bounds into Multi-class Bounds. In ICML workshop on RKHS and kernel-based methods.

@inproceedings{DoğanGlasmachersIgel2012,
	author		=	{Doğan, Ü. and Glasmachers, T. and Igel, C.},
	title		=	{Turning Binary Large-margin Bounds into Multi-class Bounds},
	booktitle	=	{ICML workshop on RKHS and kernel-based methods},
	year		=	{2012},
}

Doğan, Ü., Glasmachers, T., & Igel, C. (2012). A Note on Extending Generalization Bounds for Binary Large-margin Classifiers to Multiple Classes. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD).

@inproceedings{DoğanGlasmachersIgel2012b,
	author		=	{Doğan, Ü. and Glasmachers, T. and Igel, C.},
	title		=	{A Note on Extending Generalization Bounds for Binary Large-margin Classifiers to Multiple Classes},
	booktitle	=	{Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD)},
	year		=	{2012},
}

Glasmachers, T. (2012). Convergence of the IGO-Flow of Isotropic Gaussian Distributions on Convex Quadratic Problems. In C. C. Coello, Cutello, V., Deb, K., Forrest, S., Nicosia, G., & Pavone, M. (Eds.), Parallel Problem Solving from Nature (PPSN). Springer.

@inproceedings{Glasmachers2012,
	author		=	{Glasmachers, T.},
	title		=	{Convergence of the IGO-Flow of Isotropic Gaussian Distributions on Convex Quadratic Problems},
	booktitle	=	{Parallel Problem Solving from Nature (PPSN)},
	editor		=	{Coello, C. Coello and Cutello, V. and Deb, K. and Forrest, S. and Nicosia, G. and Pavone, M.},
	publisher	=	{Springer},
	year		=	{2012},
}

Glasmachers, T., Koutník, J., & Schmidhuber, J. (2012). Kernel Representations for Evolving Continuous Functions. Journal of Evolutionary Intelligence, 5(3), 171–187. http://doi.org/10.1007/s12065-012-0070-y

@article{GlasmachersKoutníkSchmidhuber2012,
	author		=	{Glasmachers, T. and Koutník, J. and Schmidhuber, J.},
	title		=	{Kernel Representations for Evolving Continuous Functions},
	journal		=	{Journal of Evolutionary Intelligence},
	volume		=	{5},
	number		=	{3},
	pages		=	{171–187},
	year		=	{2012},
	doi		=	{10.1007/s12065-012-0070-y},
}

Cuccu, G., Gomez, F., & Glasmachers, T.. (2011). Novelty Restarts for Evolution Strategies. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC). IEEE.

@inproceedings{CuccuGomezGlasmachers2011,
	author		=	{Cuccu, G. and Gomez, F. and Glasmachers, T.},
	title		=	{Novelty Restarts for Evolution Strategies},
	booktitle	=	{Proceedings of the IEEE Congress on Evolutionary Computation (CEC)},
	publisher	=	{IEEE},
	year		=	{2011},
}

Cuccu, G., Gomez, F., & Glasmachers, T.. (2011). Novelty Restarts for Evolution Strategies. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC). IEEE.

@inproceedings{CuccuGomezGlasmachers2011b,
	author		=	{Cuccu, G. and Gomez, F. and Glasmachers, T.},
	title		=	{Novelty Restarts for Evolution Strategies},
	booktitle	=	{Proceedings of the IEEE Congress on Evolutionary Computation (CEC)},
	publisher	=	{IEEE},
	year		=	{2011},
}

Doğan, Ü., Glasmachers, T., & Igel, C. (2011). Fast Training of Multi-Class Support Vector Machines (No. 2011/3). Department of Computer Science, University of Copenhagen.

@techreport{DoğanGlasmachersIgel2011,
	author		=	{Doğan, Ü. and Glasmachers, T. and Igel, C.},
	title		=	{Fast Training of Multi-Class Support Vector Machines},
	institution	=	{Department of Computer Science, University of Copenhagen},
	number		=	{2011/3},
	year		=	{2011},
}

Glasmachers, T., & Schmidhuber, J. (2011). Optimal Direct Policy Search. In Proceedings of the 4th Conference on Artificial General Intelligence (AGI).

@inproceedings{GlasmachersSchmidhuber2011,
	author		=	{Glasmachers, T. and Schmidhuber, J.},
	title		=	{Optimal Direct Policy Search},
	booktitle	=	{Proceedings of the 4th Conference on Artificial General Intelligence (AGI)},
	year		=	{2011},
}

Graziano, V., Glasmachers, T., Schaul, T., Pape, L., Cuccu, G., Leitner, J., & Schmidhuber, J. (2011). Artificial Curiosity for Autonomous Space Exploration. ACTA FUTURA.

@article{GrazianoGlasmachersSchaulEtAl2011,
	author		=	{Graziano, V. and Glasmachers, T. and Schaul, T. and Pape, L. and Cuccu, G. and Leitner, J. and Schmidhuber, J.},
	title		=	{Artificial Curiosity for Autonomous Space Exploration},
	journal		=	{ACTA FUTURA},
	year		=	{2011},
}

Schaul, T., Glasmachers, T., & Schmidhuber, J. (2011). High Dimensions and Heavy Tails for Natural Evolution Strategies. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO).

@inproceedings{SchaulGlasmachersSchmidhuber2011,
	author		=	{Schaul, T. and Glasmachers, T. and Schmidhuber, J.},
	title		=	{High Dimensions and Heavy Tails for Natural Evolution Strategies},
	booktitle	=	{Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)},
	year		=	{2011},
}

Schaul, T., Pape, L., Glasmachers, T., Graziano, V., & Schmidhuber, J. (2011). Coherence Progress: A Measure of Interestingness Based on Fixed Compressors. In Proceedings of the 4th Conference on Artificial General Intelligence (AGI).

@inproceedings{SchaulPapeGlasmachersEtAl2011,
	author		=	{Schaul, T. and Pape, L. and Glasmachers, T. and Graziano, V. and Schmidhuber, J.},
	title		=	{Coherence Progress: A Measure of Interestingness Based on Fixed Compressors},
	booktitle	=	{Proceedings of the 4th Conference on Artificial General Intelligence (AGI)},
	year		=	{2011},
}

Glasmachers, T. (2010). Universal Consistency of Multi-Class Support Vector Classification. In Advances in Neural Information Processing Systems (NIPS).

@inproceedings{Glasmachers2010,
	author		=	{Glasmachers, T.},
	title		=	{Universal Consistency of Multi-Class Support Vector Classification},
	booktitle	=	{Advances in Neural Information Processing Systems (NIPS)},
	year		=	{2010},
}

Glasmachers, T., & Igel, C. (2010). Maximum Likelihood Model Selection for 1-Norm Soft Margin SVMs with Multiple Parameters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(8), 1522–1528.

@article{GlasmachersIgel2010,
	author		=	{Glasmachers, T. and Igel, C.},
	title		=	{Maximum Likelihood Model Selection for 1-Norm Soft Margin SVMs with Multiple Parameters},
	journal		=	{IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume		=	{32},
	number		=	{8},
	pages		=	{1522–1528},
	year		=	{2010},
}

Glasmachers, T., Schaul, T., & Schmidhuber, J. (2010). A Natural Evolution Strategy for Multi-Objective Optimization. In Parallel Problem Solving from Nature (PPSN). Springer.

@inproceedings{GlasmachersSchaulSchmidhuber2010,
	author		=	{Glasmachers, T. and Schaul, T. and Schmidhuber, J.},
	title		=	{A Natural Evolution Strategy for Multi-Objective Optimization},
	booktitle	=	{Parallel Problem Solving from Nature (PPSN)},
	publisher	=	{Springer},
	year		=	{2010},
}

Glasmachers, T., Schaul, T., Sun, Y., Wierstra, D., & Schmidhuber, J. (2010). Exponential Natural Evolution Strategies. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO).

@inproceedings{GlasmachersSchaulSunEtAl2010,
	author		=	{Glasmachers, T. and Schaul, T. and Sun, Y. and Wierstra, D. and Schmidhuber, J.},
	title		=	{Exponential Natural Evolution Strategies},
	booktitle	=	{Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)},
	year		=	{2010},
}

Sun, Y., Glasmachers, T., Schaul, T., & Schmidhuber, J. (2010). Frontier Search. In Proceedings of the 3rd Conference on Artificial General Intelligence (AGI).

@inproceedings{SunGlasmachersSchaulEtAl2010,
	author		=	{Sun, Y. and Glasmachers, T. and Schaul, T. and Schmidhuber, J.},
	title		=	{Frontier Search},
	booktitle	=	{Proceedings of the 3rd Conference on Artificial General Intelligence (AGI)},
	year		=	{2010},
}

Glasmachers, T. (2008). On related violating pairs for working set selection in SMO algorithms. In M. Verleysen (Ed.), Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN). d-side publications.

@inproceedings{Glasmachers2008,
	author		=	{Glasmachers, T.},
	title		=	{On related violating pairs for working set selection in SMO algorithms},
	booktitle	=	{Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN)},
	editor		=	{Verleysen, M.},
	publisher	=	{d-side publications},
	year		=	{2008},
}

Glasmachers, T. (2008). Gradient Based Optimization of Support Vector Machines. Doctoral thesis, Fakultät für Mathematik, Ruhr-Universität Bochum, Germany.

@phdthesis{Glasmachers2008b,
	author		=	{Glasmachers, T.},
	title		=	{Gradient Based Optimization of Support Vector Machines},
	school		=	{Fakultät für Mathematik, Ruhr-Universität Bochum, Germany},
	year		=	{2008},
}

Glasmachers, T., & Igel, C. (2008). Second-Order SMO Improves SVM Online and Active Learning. Neural Computation, 20(2), 374–382.

@article{GlasmachersIgel2008,
	author		=	{Glasmachers, T. and Igel, C.},
	title		=	{Second-Order SMO Improves SVM Online and Active Learning},
	journal		=	{Neural Computation},
	volume		=	{20},
	number		=	{2},
	pages		=	{374–382},
	year		=	{2008},
}

Glasmachers, T., & Igel, C. (2008). Uncertainty Handling in Model Selection for Support Vector Machines. In G. Rudolph, Jansen, T., Lucas, S., Poloni, C., & Beume, N. (Eds.), Parallel Problem Solving from Nature (PPSN) (pp. 185–194). Springer.

@inproceedings{GlasmachersIgel2008b,
	author		=	{Glasmachers, T. and Igel, C.},
	title		=	{Uncertainty Handling in Model Selection for Support Vector Machines},
	booktitle	=	{Parallel Problem Solving from Nature (PPSN)},
	editor		=	{Rudolph, G. and Jansen, T. and Lucas, S. and Poloni, C. and Beume, N.},
	pages		=	{185–194},
	publisher	=	{Springer},
	year		=	{2008},
}

Igel, C., Heidrich-Meisner, V., & Glasmachers, T.. (2008). Shark. Journal of Machine Learning Research, 9, 993–996.

@article{IgelHeidrich-MeisnerGlasmachers2008,
	author		=	{Igel, C. and Heidrich-Meisner, V. and Glasmachers, T.},
	title		=	{Shark},
	journal		=	{Journal of Machine Learning Research},
	volume		=	{9},
	pages		=	{993–996},
	year		=	{2008},
}

Igel, C., Glasmachers, T., Mersch, B., Pfeifer, N., & Meinicke, P. (2007). Gradient-Based Optimization of Kernel-Target Alignment for Sequence Kernels Applied to Bacterial Gene Start Detection. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 4(2), 216–226.

@article{IgelGlasmachersMerschEtAl2007,
	author		=	{Igel, C. and Glasmachers, T. and Mersch, B. and Pfeifer, N. and Meinicke, P.},
	title		=	{Gradient-Based Optimization of Kernel-Target Alignment for Sequence Kernels Applied to Bacterial Gene Start Detection},
	journal		=	{IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)},
	volume		=	{4},
	number		=	{2},
	pages		=	{216–226},
	year		=	{2007},
}

Mersch, B., Glasmachers, T., Meinicke, P., & Igel, C. (2007). Evolutionary Optimization of Sequence Kernels for Detection of Bacterial Gene Starts. International Journal of Neural Systems, 17(5), 369–381.

@article{MerschGlasmachersMeinickeEtAl2007,
	author		=	{Mersch, B. and Glasmachers, T. and Meinicke, P. and Igel, C.},
	title		=	{Evolutionary Optimization of Sequence Kernels for Detection of Bacterial Gene Starts},
	journal		=	{International Journal of Neural Systems},
	volume		=	{17},
	number		=	{5},
	pages		=	{369–381},
	year		=	{2007},
}

Glasmachers, T. (2006). Degeneracy in Model Selection for SVMs with Radial Gaussian Kernel. In M. Verleysen (Ed.), Proceedings of the 14th European Symposium on Artificial Neural Networks (ESANN). d-side publications.

@inproceedings{Glasmachers2006,
	author		=	{Glasmachers, T.},
	title		=	{Degeneracy in Model Selection for SVMs with Radial Gaussian Kernel},
	booktitle	=	{Proceedings of the 14th European Symposium on Artificial Neural Networks (ESANN)},
	editor		=	{Verleysen, M.},
	publisher	=	{d-side publications},
	year		=	{2006},
}

Glasmachers, T., & Igel, C. (2006). Maximum-Gain Working Set Selection for Support Vector Machines. Journal of Machine Learning Research, 7, 1437–1466.

@article{GlasmachersIgel2006,
	author		=	{Glasmachers, T. and Igel, C.},
	title		=	{Maximum-Gain Working Set Selection for Support Vector Machines},
	journal		=	{Journal of Machine Learning Research},
	volume		=	{7},
	pages		=	{1437–1466},
	year		=	{2006},
}

Mersch, B., Glasmachers, T., Meinicke, P., & Igel, C. (2006). Evolutionary Optimization of Sequence Kernels for Detection of Bacterial Gene Starts. In Proceedings of the 16th International Conference on Artificial Neural Networks (ICANN). Springer-Verlag.

@inproceedings{MerschGlasmachersMeinickeEtAl2006,
	author		=	{Mersch, B. and Glasmachers, T. and Meinicke, P. and Igel, C.},
	title		=	{Evolutionary Optimization of Sequence Kernels for Detection of Bacterial Gene Starts},
	booktitle	=	{Proceedings of the 16th International Conference on Artificial Neural Networks (ICANN)},
	publisher	=	{Springer-Verlag},
	year		=	{2006},
}

Glasmachers, T., & Igel, C. (2005). Gradient-based Adaptation of General Gaussian Kernels. Neural Computation, 17(10), 2099–2105.

@article{GlasmachersIgel2005,
	author		=	{Glasmachers, T. and Igel, C.},
	title		=	{Gradient-based Adaptation of General Gaussian Kernels},
	journal		=	{Neural Computation},
	volume		=	{17},
	number		=	{10},
	pages		=	{2099–2105},
	year		=	{2005},
}

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