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23rd ESANN 2015: Bruges, Belgium
- 23rd European Symposium on Artificial Neural Networks, ESANN 2015, Bruges, Belgium, April 22-24, 2015. 2015
Prototype-based and weightless models
- David Nebel, Thomas Villmann:
Median-LVQ for classification of dissimilarity data based on ROC-optimization. - Lydia Fischer, Barbara Hammer, Heiko Wersing:
Certainty-based prototype insertion/deletion for classification with metric adaptation. - Kristin Domaschke, Marika Kaden, Mandy Lange, Thomas Villmann:
Learning matrix quantization and variants of relevance learning. - Daniel Nascimento, Rafael Lima de Carvalho, Félix Mora-Camino, Priscila M. V. Lima, Felipe M. G. França:
A WiSARD-based multi-term memory framework for online tracking of objects. - Massimo De Gregorio, Maurizio Giordano:
Memory Transfer in DRASiW-like Systems. - Ernest Mwebaze, Gjalt Bearda, Michael Biehl, Dietlind Zühlke:
Combining dissimilarity measures for prototype-based classification.
Emerging techniques and applications in multi-objective reinforcement learning
- Madalina M. Drugan:
Multi-objective optimization perspectives on reinforcement learning algorithms using reward vectors. - Saba Q. Yahyaa, Bernard Manderick:
Thompson Sampling for Multi-Objective Multi-Armed Bandits Problem. - Chiel Kooijman, Maarten de Waard, Maarten Inja, Diederik M. Roijers, Shimon Whiteson:
Pareto Local Search for MOMDP Planning. - Nixon K. Ronoh, Reuben Odoyo, Edna Milgo, Madalina M. Drugan, Bernard Manderick:
Bernoulli bandits: an empirical comparison.
Sequence learning and time series
- Sebastian Otte, Fabian Becker, Martin V. Butz, Marcus Liwicki, Andreas Zell:
Learning Recurrent Dynamics using Differential Evolution. - Nils André Treiber, Stephan Späth, Justin Heinermann, Lueder von Bremen, Oliver Kramer:
Comparison of Numerical Models and Statistical Learning for Wind Speed Prediction. - Hélène Le Cadre, Ignacio Aravena, Anthony Papavasiliou:
Solar PV Power Forecasting Using Extreme Learning Machine and Information Fusion. - Hande Topa, Antti Honkela:
Gaussian process modelling of multiple short time series. - Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, Puneet Agarwal:
Long Short Term Memory Networks for Anomaly Detection in Time Series. - Pekka Wartiainen, Tommi Kärkkäinen:
Hierarchical, prototype-based clustering of multiple time series with missing values.
Regression and prediction
- Jens Schreiter, Duy Nguyen-Tuong, Heiner Markert, Michael Hanselmann, Marc Toussaint:
Fast greedy insertion and deletion in sparse Gaussian process regression. - Thomas Hecht, Mathieu Lefort, Alexander Gepperth:
Using self-organizing maps for regression: the importance of the output function. - Arnaud De Myttenaere, Boris Golden, Bénédicte Le Grand, Fabrice Rossi:
Using the Mean Absolute Percentage Error for Regression Models. - Samuel Branders, Benoît Frénay, Pierre Dupont:
Survival Analysis with Cox Regression and Random Non-linear Projections. - Kaushala Dias, Terry Windeatt:
Ensemble Learning with Dynamic Ordered Pruning for Regression. - Honovan P. Rocha, Marcelo Azevedo Costa, Antônio P. Braga:
Training Multi-Layer Perceptron with Multi-Objective Optimization and Spherical Weights Representation. - Arnaud De Myttenaere, Boris Golden, Bénédicte Le Grand, Fabrice Rossi:
Reducing offline evaluation bias of collaborative filtering. - Yuan Yuan Chai, Jun Chen, Wei Luo:
A new fuzzy neural system with applications. - Héctor Ruiz, Paulo Lisboa, Paul Neilson, Warren Gregson:
Measuring scoring efficiency through goal expectancy estimation. - Maria Yli-Heikkilä, Jukka Tauriainen, Mika Sulkava:
Predicting the profitability of agricultural enterprises in dairy farming. - Roberto Zanetti Freire, Gerson H. dos Santos, Leandro dos Santos Coelho, Viviana Cocco Mariani, Divani da S. Carvalho:
The use of RBF neural network to predict building's corners hygrothermal behavior. - Johannes Pohl, Andreas Noack:
I see you: on neural networks for indoor geolocation.
Feature and kernel learning
- Verónica Bolón-Canedo, Michele Donini, Fabio Aiolli:
Feature and kernel learning. - Robert Lieck, Marc Toussaint:
Discovering temporally extended features for reinforcement learning in domains with delayed causalities. - Davide Bacciu, Filippo Benedetti, Alessio Micheli:
ESNigma: efficient feature selection for echo state networks. - Beatriz Remeseiro, Verónica Bolón-Canedo, Amparo Alonso-Betanzos, Manuel G. Penedo:
Learning features on tear film lipid layer classification. - Andrey Filchenkov, Vladislav Dolganov, Ivan Smetannikov:
PCA-based algorithm for feature score measures ensemble construction.
Graphs in machine learning
- Pierre Latouche, Fabrice Rossi:
Graphs in machine learning. An introduction. - Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti:
Exploiting the ODD framework to define a novel effective graph kernel. - Marco Corneli, Pierre Latouche, Fabrice Rossi:
Exact ICL maximization in a non-stationary time extension of latent block model for dynamic networks. - Rawya Zreik, Pierre Latouche, Charles Bouveyron:
A State-Space Model for the Dynamic Random Subgraph Model. - Luiz C. B. Torres, Cristiano Leite Castro
, Antônio P. Braga:
Gabriel Graph for Dataset Structure and Large Margin Classification: A Bayesian Approach.
Manifold learning and optimization
- Oliver Kramer:
Supervised Manifold Learning with Incremental Stochastic Embeddings. - Guifang Zhou, Wen Huang, Kyle A. Gallivan, Paul Van Dooren, Pierre-Antoine Absil:
Rank-constrained optimization: a Riemannian manifold approach. - Jérôme Fellus, David Picard, Philippe-Henri Gosselin:
Asynchronous decentralized convex optimization through short-term gradient averaging.
Feature and model selection, sparse models
- Luca Oneto, Bernardo Pilarz, Alessandro Ghio, Davide Anguita:
Model Selection for Big Data: Algorithmic Stability and Bag of Little Bootstraps on GPUs. - Carlos M. Alaíz, Alberto Torres, José R. Dorronsoro:
Solving constrained Lasso and Elastic Net using nu-SVMs. - Tommi Kärkkäinen:
Assessment of feature saliency of MLP using analytic sensitivity. - Jean Golay, Michael Leuenberger, Mikhail F. Kanevski:
Morisita-based feature selection for regression problems. - Suwimol Jungjit, Alex Alves Freitas:
A new genetic algorithm for multi-label correlation-based feature selection. - Tsirizo Rabenoro, Jérôme Lacaille, Marie Cottrell, Fabrice Rossi:
Search Strategies for Binary Feature Selection for a Naive Bayes Classifier.
Advances in learning analytics and educational data mining
- Mehrnoosh Vahdat, Alessandro Ghio, Luca Oneto, Davide Anguita, Mathias Funk, Matthias Rauterberg:
Advances in learning analytics and educational data mining. - Benjamin Paassen, Bassam Mokbel, Barbara Hammer:
Adaptive structure metrics for automated feedback provision in Java programming. - Mehrnoosh Vahdat, Luca Oneto, Alessandro Ghio, Davide Anguita, Mathias Funk, Matthias Rauterberg:
Human Algorithmic Stability and Human Rademacher Complexity. - Nicolae-Bogdan Sara, Rasmus Halland, Christian Igel, Stephen Alstrup:
High-School Dropout Prediction Using Machine Learning: A Danish Large-scale Study. - Minoru Nakayama, Kouichi Mutsuura, Hiroh Yamamoto:
The prediction of learning performance using features of note taking activities. - Virpi Kalakoski, Henriikka Ratilainen, Linda Drupsteen:
Enhancing learning at work. How to combine theoretical and data-driven approaches, and multiple levels of data? - Mirka Saarela, Tommi Kärkkäinen:
Weighted Clustering of Sparse Educational Data.
Classification
- David Pinto, André P. Lemos, Antônio P. Braga:
An affinity matrix approach for structure selection of extreme learning machines. - Jakramate Bootkrajang:
A generalised label noise model for classification. - Verónica Bolón-Canedo, Alba Fernández, Amparo Alonso-Betanzos, Marcos Ortega, Manuel G. Penedo:
On the use of machine learning techniques for the analysis of spontaneous reactions in automated hearing assessment. - Jafar Tanha, Jesse de Does, Katrien Depuydt:
Combining higher-order N-grams and intelligent sample selection to improve language modeling for Handwritten Text Recognition. - Saikat Basu, Manohar Karki, Sangram Ganguly, Robert DiBiano, Supratik Mukhopadhyay, Ramakrishna R. Nemani:
Learning Sparse Feature Representations using Probabilistic Quadtrees and Deep Belief Nets. - Denis Rousselle, Stéphane Canu:
Optimal transport for semi-supervised domain adaptation. - Alexander Gepperth, Mathieu Lefort, Thomas Hecht:
Resource-efficient Incremental learning in very high dimensions. - Md. Nasim Adnan, Md Zahidul Islam:
One-vs-all binarization technique in the context of random forest. - Md. Nasim Adnan, Md Zahidul Islam:
Improving the random forest algorithm by randomly varying the size of the bootstrap samples for low dimensional data sets. - Stéphan Clémençon, Sylvain Robbiano:
An Ensemble Learning Technique for Multipartite Ranking. - Hongliang Zhong, Emmanuel Daucé, Liva Ralaivola:
Online multiclass learning with "bandit" feedback under a Passive-Aggressive approach. - Galina V. Veres, Zoheir A. Sabeur:
Data Analytics for Drilling Operational States Classifications. - Woubishet Taffese, Esko Sistonen, Jari A. Puttonen:
Prediction of concrete carbonation depth using decision trees. - Ferhat Attal, Abderrahmane Boubezoul, Allou Samé, Latifa Oukhellou:
Powered-Two-Wheeler safety critical events recognition using a mixture model with quadratic logistic functions.
Image processing and vision systems
- Kishore Konda, Pramod Chandrashekhariah, Roland Memisevic, Jochen Triesch:
Real-time activity recognition via deep learning of motion features. - Luepol Pipanmaekaporn, Ludmilla Tajtelbom, Vincent Guigue, Thierry Artières:
Designing semantic feature spaces for brain-reading. - Marcelo Borghetti Soares, Pablo V. A. Barros, German Ignacio Parisi, Stefan Wermter:
Learning objects from RGB-D sensors using point cloud-based neural networks. - Dalia Marcela Rojas-Castro, Arnaud Revel, Michel Ménard:
A Robust Neural Robot Navigation Using a Combination of Deliberative and Reactive Control Architectures. - Sebastian Otte, Stefan Laible, Richard Hanten, Marcus Liwicki, Andreas Zell:
Robust Visual Terrain Classification with Recurrent Neural Networks. - Walther Maciel, Cristina Nader Vasconcelos, Pedro Mario Silva, Marcelo Gattass:
Revisiting ant colony algorithms to seismic faults detection. - Hannes Schulz, Nico Höft, Sven Behnke:
Depth and height aware semantic RGB-D perception with convolutional neural networks. - Thomas Kopinski, Alexander Gepperth, Uwe Handmann:
A simple technique for improving multi-class classification with neural networks. - Doreen Jirak, Pablo V. A. Barros, Stefan Wermter:
Dynamic gesture recognition using Echo State Networks. - Andre Lemme, Jochen J. Steil:
A flat neural network architecture to represent movement primitives with integrated sequencing.
Unsupervised nonlinear dimensionality reduction
- Kerstin Bunte, John Aldo Lee:
Unsupervised dimensionality reduction: the challenge of big data visualization. - Nikolaos Gianniotis
Autoencoding time series for visualisation. - Carlos M. Alaíz, Ángela Fernández, José R. Dorronsoro:
Diffusion Maps parameters selection based on neighbourhood preservation. - Patrick Blöbaum, Alexander Schulz, Barbara Hammer:
Unsupervised Dimensionality Reduction for Transfer Learning. - Aurore Payen, Ludovic Journaux, Clément Delion, Lucile Sautot, Bruno Faivre:
Efficient unsupervised clustering for spatial birds population analysis along the river Loire. - Clément Delion, Ludovic Journaux, Aurore Payen, Lucile Sautot, Emmanuel Chevigny, Pierre Curmi:
NLDR methods for high dimensional NIRS dataset : application to vineyard soils characterization. - Diego Hernán Peluffo-Ordóñez, Juan Carlos Alvarado-Pérez, John Aldo Lee, Michel Verleysen:
Geometrical homotopy for data visualization.
Unsupervised learning
- Anthony Coutant, Hoel Le Capitaine, Philippe Leray:
On the equivalence between regularized NMF and similarity-augmented graph partitioning. - Raghvendra Mall, Rocco Langone, Johan A. K. Suykens:
Ranking Overlap and Outlier Points in Data using Soft Kernel Spectral Clustering. - Kaj-Mikael Björk, Patrick Kouontchou, Amaury Lendasse, Yoan Miché, Bertrand Maillet:
Towards a Tomographic Index of Systemic Risk Measures. - Rodrigo Echeveste, Claudius Gros:
An objective function for self-limiting neural plasticity rules.
Kernel methods
- Frank-Michael Schleif
Probabilistic Classification Vector Machine at large scale. - Julien Audiffren, Hachem Kadri:
Online Learning with Operator-valued Kernels. - Patric Nader, Paul Honeine, Pierre Beauseroy:
Online One-class Classification for Intrusion Detection Based on the Mahalanobis Distance. - Péricles B. C. de Miranda, Paulo Ricardo da Silva Soares, Ricardo B. C. Prudêncio:
I/S-Race: An iterative Multi-Objective Racing Algorithm for the SVM Parameter Selection Problem. - Markus Kächele, Günther Palm, Friedhelm Schwenker:
SMO Lattices for the Parallel Training of Support Vector Machines. - Fei Zhu, Paul Honeine:
Pareto front of bi-objective kernel-based nonnegative matrix factorization. - Senka Krivic, Sándor Szedmák, Hanchen Xiong, Justus H. Piater:
Learning missing edges via kernels in partially-known graphs.
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