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ESANN 2013: Bruges, Belgium
- 21st European Symposium on Artificial Neural Networks, ESANN 2013, Bruges, Belgium, April 24-26, 2013. 2013
Machine Learning Methods for Processing and Analysis of Hyperspectral Data
- Thomas Villmann, Marika Kästner, Andreas Backhaus, Udo Seiffert:
Processing Hyperspectral Data in Machine Learning. - Michele Volpi, Giona Matasci, Mikhail F. Kanevski, Devis Tuia:
Multi-view feature extraction for hyperspectral image classification. - Martin Riedel, Fabrice Rossi, Marika Kästner, Thomas Villmann:
Regularization in relevance learning vector quantization using l1-norms.
Recurrent networks and modeling
- Kevin Swingler, Leslie S. Smith:
Mixed order associative networks for function approximation, optimisation and sampling. - Stefan Glüge, Ronald Böck, Andreas Wendemuth:
Auto-encoder pre-training of segmented-memory recurrent neural networks. - Levy Boccato, Daniel G. Silva, Denis G. Fantinato, Kenji Nose Filho, Rafael Ferrari, Romis Attux, Aline Neves, Jugurta Montalvão, João Marcos Travassos Romano:
Error entropy criterion in echo state network training. - Martin Meier, Robert Haschke, Helge J. Ritter:
Perceptual grouping through competition in coupled oscillator networks. - Niels Bloom, Mariët Theune, Franciska de Jong:
Using Wikipedia with associative networks for document classification. - Galina V. Veres, Zoheir A. Sabeur:
Automated operational states detection for drilling systems control in critical conditions. - Li Guo, Zhijun Yang, Qingbao Zhu:
Analysis of Synaptic Weight Distribution in an Izhikevich Network. - Gaetano Liborio Aiello, Valentino Romano:
Percolation model of axon guidance. - Wen-Jyi Hwang, Hao Chen:
Efficient VLSI Architecture for Spike Sorting Based on Generalized Hebbian Algorithm.
Dimensionality reduction
- Marc Strickert, Kerstin Bunte:
Soft rank neighbor embeddings. - Madalina Olteanu, Nathalie Villa-Vialaneix, Christine Cierco-Ayrolles:
Multiple Kernel Self-Organizing Maps. - Xibin Zhu, Frank-Michael Schleif
, Barbara Hammer:
Semi-Supervised Vector Quantization for proximity data. - Francisco J. García-Fernández, Michel Verleysen, John Aldo Lee, Ignacio Díaz Blanco:
Sensitivity to parameter and data variations in dimensionality reduction techniques.
Image, signal and time series analysis
- Augustin Lefèvre, François Glineur, Pierre-Antoine Absil:
A nuclear-norm based convex formulation for informed source separation. - Aurélien Hazan, Kurosh Madani:
Frequency-Dependent Peak-Over-Threshold algorithm for fault detection in the spectral domain. - Fabrice Rossi, Pierre Latouche:
Activity Date Estimation in Timestamped Interaction Networks. - Philippe Smagghe, Jean-Luc Buessler, Jean-Philippe Urban:
Novelty detection in image recognition using IRF Neural Networks properties. - Mandy Lange, Michael Biehl, Thomas Villmann:
Non-Euclidean independent component analysis and Oja's learning. - Andrés Marino Álvarez-Meza, Carlos Daniel Acosta-Medina, Germán Castellanos-Domínguez:
Automatic Singular Spectrum Analysis for Time-Series Decomposition. - Valeri Tsatsishvili, Fengyu Cong, Tuomas Puoliväli, Vinoo Alluri, Petri Toiviainen, Asoke K. Nandi, Elvira Brattico, Tapani Ristaniemi:
Dimension reduction for individual ica to decompose FMRI during real-world experiences: principal component analysis vs. canonical correlation analysis. - Fernando Mateo, Juan José Carrasco, Mónica Millán-Giraldo, Abderrahim Sellami, Pablo Escandell-Montero, José María Martínez-Martínez, Emilio Soria-Olivas:
Machine Learning Techniques for Short-Term Electric Power Demand Prediction. - Levente Orbán, Sylvain Chartier:
Unsupervised non-linear neural networks capture aspects of floral choice behaviour.
Feature selection
- Maria Fernanda B. Wanderley, Vincent Gardeux, René Natowicz, Antônio de Pádua Braga:
GA-KDE-Bayes: an evolutionary wrapper method based on non-parametric density estimation applied to bioinformatics problems. - Gauthier Doquire, Benoît Frénay, Michel Verleysen:
Risk Estimation and Feature Selection. - Mark J. Embrechts, Jonathan D. Linton, Jorge M. Santos:
Random Brains: An ensemble method for feature selection with neural networks. - Verónica Bolón-Canedo, Noelia Sánchez-Maroño, Amparo Alonso-Betanzos:
A distributed wrapper approach for feature selection. - Fernando Mateo, Mónica Millán-Giraldo, Juan José Carrasco, Enrique Montiel, José Antonio Bernabéu, José David Martín-Guerrero:
Feature Selection for Footwear Shape Estimation. - Thomas Schmid, Dorothee Günzel, Martin Bogdan:
Efficient prediction of x-axis intercepts of discrete impedance spectra. - Robert Kaltenhaeuser, Erik Schaffernicht, Frank-Florian Steege, Horst-Michael Gross:
Evolutionary computation based system decomposition with neural networks.
Reinforcement learning, control and optimization
- Emmanuel Daucé, Timothée Proix, Liva Ralaivola:
Fast online adaptivity with policy gradient: example of the BCI "P300"-speller. - Matthew Howard, Yoshihiko Nakamura:
Locally Weighted Least Squares Temporal Difference Learning. - Bastian Bischoff, Duy Nguyen-Tuong, Heiner Markert, Alois C. Knoll:
Learning control under uncertainty: A probabilistic Value-Iteration approach. - Siegmund Duell, Steffen Udluft:
Ensembles for Continuous Actions in Reinforcement Learning. - Christopher J. Gatti, Mark J. Embrechts, Jonathan D. Linton:
An empirical analysis of reinforcement learning using design of experiments. - Bastian Bischoff, Duy Nguyen-Tuong, I-Hsuan Lee, Felix Streichert, Alois C. Knoll:
Hierarchical Reinforcement Learning for Robot Navigation. - Pablo Escandell-Montero, José María Martínez-Martínez, José David Martín-Guerrero, Emilio Soria-Olivas, Juan Gómez-Sanchís:
Least-squares temporal difference learning based on extreme learning machine. - Denise Gorse:
Binary particle swarm optimisation with improved scaling behaviour. - Rafael Lima de Carvalho, Lunlong Zhong, Felipe M. G. França, Félix Mora-Camino:
Dynamic Placement with Connectivity for RSNs based on a Primal-Dual Neural Network.
Machine Learning for multimedia applications
- Philippe Henri Gosselin, David Picard:
Machine Learning and Content-Based Multimedia Retrieval. - Thomas Guthier, Steve Gerges, Volker Willert, Julian Eggert:
Learning associative spatiotemporal features with non-negative sparse coding. - Ksenia Konyushkova, Dorota Glowacka:
Content-based image retrieval with hierarchical Gaussian Process bandits with self-organizing maps.
Clustering
- Andry Njato Randriamanamihaga, Etienne Côme, Latifa Oukhellou, Gérard Govaert:
Clustering the Vélib' origin-destinations flows by means of Poisson mixture models. - Octavio Razafindramanana, Gilles Venturini:
Delaunay simplices pruning based clustering. - Johanna Baro, Etienne Côme, Patrice Aknin, Olivier Bonin:
Hierarchical and multiscale Mean Shift segmentation of population grids. - Laetitia Nouedoui, Pierre Latouche:
Bayesian non parametric inference of discrete valued networks. - Filippo Pompili, Nicolas Gillis, François Glineur, Pierre-Antoine Absil:
ONP-MF: An Orthogonal Nonnegative Matrix Factorization Algorithm with Application to Clustering. - Zalán Bodó, Lehel Csató:
Linear spectral hashing. - Diego Hernán Peluffo-Ordóñez, Andrés Eduardo Castro-Ospina, Diego Chavez-Chamorro, Carlos Daniel Acosta-Medina, Germán Castellanos-Domínguez:
Normalized cuts clustering with prior knowledge and a pre-clustering stage. - Twan van Laarhoven, Elena Marchiori:
Network community detection with edge classifiers trained on LFR graphs.
Regression and forecasting
- Armin Walter, Georgios Naros, Martin Spüler, Alireza Gharabaghi, Wolfgang Rosenstiel, Martin Bogdan:
Decoding stimulation intensity from evoked ECoG activity using support vector regression. - Andre Lemme, Klaus Neumann, René Felix Reinhart, Jochen J. Steil:
Neurally imprinted stable vector fields. - Jonas Kalderstam, Patrik Edén, Mattias Ohlsson:
Ensembles of genetically trained artificial neural networks for survival analysis. - Manuel Blum, Martin A. Riedmiller:
Optimization of Gaussian process hyperparameters using Rprop. - José Barahona da Fonseca:
Are Rosenblatt multilayer perceptrons more powerfull than sigmoidal multilayer perceptrons? From a counter example to a general result. - Abou Keita, Romain Hérault, Colas Calbrix, Stéphane Canu:
Detection and quantification in real-time polymerase chain reaction. - Fernando Mateo, Juan José Carrasco, Mónica Millán-Giraldo, Abderrahim Sellami, Pablo Escandell-Montero, José María Martínez-Martínez, Emilio Soria-Olivas:
Temperature Forecast in Buildings Using Machine Learning Techniques. - Patrick Kouontchou, Amaury Lendasse, Yoan Miché, Bertrand Maillet:
Forecasting Financial Markets with Classified Tactical Signals.
Developments in kernel design
- Lluís Belanche:
Developments in kernel design. - Vicent J. Ribas Ripoll, Enrique Romero, Juan Carlos Ruiz-Rodríguez, Alfredo Vellido:
A quotient basis kernel for the prediction of mortality in severe sepsis patients. - María Pérez-Ortiz, Pedro Antonio Gutiérrez, César Hervás-Martínez:
Synthetic over-sampling in the empirical feature space. - María Pérez-Ortiz, Pedro Antonio Gutiérrez, Javier Sánchez-Monedero, César Hervás-Martínez:
Multi-scale Support Vector Machine Optimization by Kernel Target-Alignment. - Vladimer Kobayashi, Tomàs Aluja, Lluís Belanche:
Handling missing values in kernel methods with application to microbiology data.
Human Activity and Motion Disorder Recognition: towards smarter Interactive Cognitive Environments
- Jorge Luis Reyes-Ortiz, Alessandro Ghio, Xavier Parra, Davide Anguita, Joan Cabestany, Andreu Català:
Human Activity and Motion Disorder Recognition: towards smarter Interactive Cognitive Environments. - Albert Samà, Carlos Pérez-López, Daniel Rodríguez Martín, Joan Cabestany, Juan-Manuel Moreno Aróstegui, Alejandro Rodríguez-Molinero:
A heterogeneous database for movement knowledge extraction in Parkinson's disease. - Labiba Gillani Fahad, Arshad Ali, Muttukrishnan Rajarajan:
Long term analysis of daily activities in smart home. - Lei Gao, Alan Bourke, John Nelson:
Sensor Positioning for Activity Recognition Using Multiple Accelerometer-Based Sensors. - Audrey Robinel, Didier Puzenat:
Multi-user Blood Alcohol Content estimation in a realistic simulator using Artificial Neural Networks and Support Vector Machines. - Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, Jorge Luis Reyes-Ortiz:
A Public Domain Dataset for Human Activity Recognition using Smartphones. - Bernardino Romera-Paredes, Min S. H. Aung, Nadia Bianchi-Berthouze:
A One-Vs-One Classifier Ensemble With Majority Voting for Activity Recognition. - Marika Kästner, Marc Strickert, Thomas Villmann:
A sparse kernelized matrix learning vector quantization model for human activity recognition. - Attila Reiss, Gustaf Hendeby, Didier Stricker:
A competitive approach for human activity recognition on smartphones.
Classification
- Salvatore Masecchia, Saverio Salzo, Annalisa Barla, Alessandro Verri:
A dictionary learning based method for aCGH segmentation. - Davide Anguita, Alessandro Ghio, Luca Oneto, Sandro Ridella:
A Learning Machine with a Bit-Based Hypothesis Space. - Joe Staines, David Barber:
Optimization by Variational Bounding. - Santiago Murillo Rendón, Diego Hernán Peluffo-Ordóñez, Germán Castellanos-Domínguez:
support vector machine-based aproach for multi-labelers problems. - James Hogan, Peter Holland, Alex Holloway, Robert Petit, Timothy Read:
Read classification for next generation sequencing. - Agata Manolova, Anne Guérin-Dugué:
A new metric for dissimilarity data classification based on Support Vector Machines optimization. - Oliver Beyer, Philipp Cimiano:
DYNG: Dynamic Online Growing Neural Gas for stream data classification. - Klaas Dijkstra, Walter Jansen, Jaap van de Loosdrecht:
Prior knowledge in an end-user trainable machine vision framework. - Tina Geweniger, Marika Kästner, Thomas Villmann:
Border sensitive fuzzy vector quantization in semi-supervised learning. - Danilo S. Carvalho, Hugo C. C. Carneiro, Felipe M. G. França, Priscila M. V. Lima:
B-bleaching: Agile Overtraining Avoidance in the WiSARD Weightless Neural Classifier. - Douglas de O. Cardoso, João Gama, Massimo De Gregorio, Felipe M. G. França, Maurizio Giordano, Priscila M. V. Lima:
WIPS: the WiSARD Indoor Positioning System. - Van Tuc Nguyen, Ah Chung Tsoi, Markus Hagenbuchner:
Cost-sensitive cascade graph neural networks.
Sparsity for interpretation and visualization in inference models
- Vanya Van Belle, Paulo Lisboa:
Research directions in interpretable machine learning models. - Clemens Otte:
Learning regression models with guaranteed error bounds. - Daniela Hofmann, Barbara Hammer:
Sparse approximations for kernel learning vector quantization. - Alessandra Tosi, Alfredo Vellido:
Robust cartogram visualization of outliers in manifold learning. - José María Martínez-Martínez, Pablo Escandell-Montero, José David Martín-Guerrero, Joan Vila-Francés, Emilio Soria-Olivas:
ManiSonS: A New Visualization Tool for Manifold Clustering. - David L. García, Àngela Nebot, Alfredo Vellido:
Visualizing pay-per-view television customers churn using cartograms and flow maps. - Andrej Gisbrecht, Yoan Miché, Barbara Hammer, Amaury Lendasse:
Visualizing dependencies of spectral features using mutual information.
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