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ESANN 2014: Bruges, Belgium
- 22th European Symposium on Artificial Neural Networks, ESANN 2014, Bruges, Belgium, April 23-25, 2014. 2014
Advances in Spiking Neural Information Processing Systems (SNIPS)
- André Grüning, Sander M. Bohté:
Spiking Neural Networks: Principles and Challenges. - Aboozar Taherkhani, Ammar Belatreche, Yuhua Li, Liam P. Maguire:
A new biologically plausible supervised learning method for spiking neurons. - Davide Zambrano, Jaldert O. Rombouts, Cecilia Laschi, Sander M. Bohté:
Spiking AGREL. - Brian Gardner, André Grüning:
Classifying Patterns in a Spiking Neural Network. - Emmanuel Daucé:
Toward STDP-based population action in large networks of spiking neurons.
Vector quantization- and nearest neighbour-based methods
- David Nebel, Barbara Hammer, Thomas Villmann:
Supervised Generative Models for Learning Dissimilarity Data. - Lydia Fischer, Barbara Hammer, Heiko Wersing:
Rejection strategies for learning vector quantization. - Marika Kaden, Wieland Hermann, Thomas Villmann:
Optimization of General Statistical Accuracy Measures for Classification Based on Learning Vector Quantization. - Zalán Bodó, Lehel Csató:
Augmented hashing for semi-supervised scenarios. - François Fouss, Clémentine Van Parijs:
Improving accuracy by reducing the importance of hubs in nearest-neighbor recommendations. - Alexandre Wagner Chagas Faria, David Menotti, André Paim Lemos, Antônio de Pádua Braga:
A new approach for multiple instance learning based on a homogeneity bag operator.
Byte the bullet: learning on real-world computing architectures
- Alessandro Ghio, Luca Oneto:
Byte The Bullet: Learning on Real-World Computing Architectures. - Davide Anguita, Alessandro Ghio, Luca Oneto, Sandro Ridella:
Learning with few bits on small-scale devices: From regularization to energy efficiency. - Fabian Gieseke, Kai Lars Polsterer, Cosmin Eugen Oancea, Christian Igel:
Speedy greedy feature selection: Better redshift estimation via massive parallelism. - Steven Lauwereins, Komail M. H. Badami, Wannes Meert, Marian Verhelst:
Context- and cost-aware feature selection in ultra-low-power sensor interfaces. - Árpád Berta, István Hegedüs, Róbert Ormándi:
Lightning fast asynchronous distributed k-means clustering.
Reinforcement learning and optimization
- Stefan Faußer, Friedhelm Schwenker:
Selective Neural Network Ensembles in Reinforcement Learning. - Jaldert O. Rombouts, Pieter R. Roelfsema, Sander M. Bohté:
Learning resets of neural working memory. - Janis Zuters:
Naive Augmenting Q-Learning to Process Feature-Based Representations of States. - Jean-Joseph Christophe, Jérémie Decock, Olivier Teytaud:
Direct Model-Predictive Control. - Pablo Escandell-Montero, José María Martínez-Martínez, Emilio Soria-Olivas, Joan Vila-Francés, José David Martín-Guerrero:
Ensembles of extreme learning machine networks for value prediction. - Christopher J. Gatti, Mark J. Embrechts:
An application of the temporal difference algorithm to the truck backer-upper problem. - Barry D. Nichols, Dimitris C. Dracopoulos:
Application of Newton's Method to action selection in continuous state- and action-space reinforcement learning. - Saba Q. Yahyaa, Madalina M. Drugan, Bernard Manderick:
Linear Scalarized Knowledge Gradient in the Multi-Objective Multi-Armed Bandits Problem. - Kiril Ralinovski:
Improving the firefly algorithm through the Barnes-Hut tree code. - Carlos Eduardo Klein, Leandro dos Santos Coelho, Ângelo M. O. Sant'Anna, Roberto Zanetti Freire, Viviana Cocco Mariani:
Improved Cat Swarm Optimization approach applied to reliability-redundancy problem.
Nonlinear dimensionality reduction
- Alexander Schulz, Andrej Gisbrecht, Barbara Hammer:
Relevance Learning for Dimensionality Reduction. - Jérôme Fellus, David Picard, Philippe Henri Gosselin:
Dimensionality reduction in decentralized networks by Gossip aggregation of principal components analyzers. - John Aldo Lee, Diego Hernán Peluffo-Ordóñez, Michel Verleysen:
Multiscale stochastic neighbor embedding: Towards parameter-free dimensionality reduction. - Ignacio Díaz Blanco, Abel Alberto Cuadrado Vega, Daniel Pérez, Francisco J. García-Fernández, Michel Verleysen:
Interactive dimensionality reduction for visual analytics. - Diego Hernán Peluffo-Ordóñez, John Aldo Lee, Michel Verleysen:
Recent methods for dimensionality reduction: A brief comparative analysis.
Signal and temporal processing
- Emilie Renard, Andrew E. Teschendorff, Pierre-Antoine Absil:
Capturing confounding sources of variation in DNA methylation data by spatiotemporal independent component analysis. - Denis Baheux, Hervé Frezza-Buet, Jérémy Fix:
Towards an effective multi-map self organizing recurrent neuronal network. - João F. L. Oliveira, Teresa Bernarda Ludermir:
Iterative ARIMA-multiple support vector regression models for long term time series prediction. - Tommi Kärkkäinen, Alexandr V. Maslov, Pekka Wartiainen:
Region of interest detection using MLP. - Everton Z. Nadalin, Rodrigo C. Silva, Romis Attux, João M. T. Romano:
Analysis of the Weighted Fuzzy C-means in the problem of source location. - Marta Kolasa, Rafal Dlugosz, Tomasz Talaska, Witold Pedrycz:
An Optimized Learning Algorithm Based on Linear Filters Suitable for Hardware implemented Self-Organizing Maps. - Marc Plouvin, Abdelhakim Limem, Matthieu Puigt, Gilles Delmaire, Gilles Roussel, Dominique Courcot:
Enhanced NMF initialization using a physical model for pollution source apportionment. - José Barahona da Fonseca:
A New Error-Correcting Syndrome Decoder with Retransmit Signal Implemented with an Hardlimit Neural Network.
Learning of structured and non-standard data
- Frank-Michael Schleif
, Peter Tiño, Thomas Villmann:
Recent trends in learning of structured and non-standard data. - Fengzhen Tang, Peter Tiño, Pedro Antonio Gutiérrez, Huanhuan Chen:
Support Vector Ordinal Regression using Privileged Information. - Herbert Teun Kruitbosch, Ioannis Giotis, Michael Biehl:
Segmented shape-symbolic time series representation. - Bassam Mokbel, Benjamin Paaßen, Barbara Hammer:
Adaptive distance measures for sequential data. - Mandy Lange, Dietlind Zühlke, Olaf Holz, Thomas Villmann:
Applications of lp-Norms and their Smooth Approximations for Gradient Based Learning Vector Quantization. - Kristin Domaschke, André Roßberg, Thomas Villmann:
Utilization of Chemical Structure Information for Analysis of Spectra Composites. - Christopher Bowles, James Hogan:
Weighted tree kernels for sequence analysis.
Kernel methods
- Fabio Aiolli, Michele Donini:
Easy multiple kernel learning. - Hanchen Xiong, Sándor Szedmák, Justus H. Piater:
Joint SVM for Accurate and Fast Image Tagging. - Jérôme Paul, Pierre Dupont:
Kernel methods for mixed feature selection. - Nicolas Chrysanthos, Pierre Beauseroy, Hichem Snoussi, Edith Grall-Maës, Fabrice Ferrand:
The one-sided mean kernel: a positive definite kernel for time series. - Antoine Lachaud, Stéphane Canu, David Mercier, Frédéric Suard:
A robust regularization path for the Doubly Regularized Support Vector Machine. - Tan Vo, Dat Tran, Wanli Ma:
Tensor decomposition of dense SIFT descriptors in object recognition. - Péricles B. C. de Miranda, Paulo Ricardo da Silva Soares, Ricardo B. C. Prudêncio:
Fine-tuning of support vector machine parameters using racing algorithms. - Ricardo Gamelas Sousa, Ajalmar R. da Rocha Neto, Guilherme A. Barreto, Jaime S. Cardoso, Miguel T. Coimbra:
reject option paradigm for the reduction of support vectors. - Ali Fallah Tehrani, Marc Strickert, Eyke Hüllermeier:
The Choquet kernel for monotone data.
Learning and Modeling Big Data
- Barbara Hammer, Haibo He, Thomas Martinetz:
Learning and modeling big data. - Raghvendra Mall, Rocco Langone, Johan A. K. Suykens:
Agglomerative hierarchical kernel spectral clustering for large scale networks. - Frank-Michael Schleif
Proximity learning for non-standard big data. - Majdi Mansouri, Marie-France Destain, Benjamin Dumont:
Predicting Grain Protein Content of Winter Wheat.
Classification
- Monica Bianchini, Franco Scarselli:
On the complexity of shallow and deep neural network classifiers. - Emmanuel Daucé, Eoin M. Thomas:
Evidence build-up facilitates on-line adaptivity in dynamic environments: example of the BCI P300-speller. - Dominik Schnitzer, Arthur Flexer:
Choosing the Metric in High-Dimensional Spaces Based on Hub Analysis. - Meriem El Azami, Carole Lartizien, Stéphane Canu:
Robust outlier detection with L0-SVDD. - Verónica Bolón-Canedo, Noelia Sánchez-Maroño, Joana Cerviño-Rabuñal:
Toward parallel feature selection from vertically partitioned data. - Iago Porto-Díaz, David Martínez-Rego, Oscar Fontenla-Romero, Amparo Alonso-Betanzos:
Modeling consumption of contents and advertising in online newspapers. - Vilen Jumutc, Johan A. K. Suykens:
Reweighted l1 Dual Averaging Approach for Sparse Stochastic Learning. - Dinh Q. Phung, Dat Tran, Wanli Ma, Phuoc Nguyen, Tien Pham:
Using Shannon Entropy as EEG Signal Feature for Fast Person Identification. - Sébastien Piérard, Rémy Phan-Ba, Marc Van Droogenbroeck:
Machine learning techniques to assess the performance of a gait analysis system. - José A. Seoane, Ian N. M. Day, Juan P. Casas, Colin Campbell, Tom R. Gaunt:
A Random Forest proximity matrix as a new measure for gene annotation. - Louis-Charles Caron, Yang Song, David Filliat, Alexander Gepperth:
Neural network based 2D/3D fusion for robotic object recognition. - Mathieu Lefort, Alexander Gepperth:
Discrimination of visual pedestrians data by combining projection and prediction learning. - German Ignacio Parisi, Pablo V. A. Barros, Stefan Wermter:
FINGeR: Framework for interactive neural-based gesture recognition. - Thomas Guthier, Volker Willert, Julian Eggert:
Beyond histograms: why learned structure-preserving descriptors outperform HOG. - Lahouari Ghouti, Abdullah Owaidh:
NMF-Density: NMF-Based Breast Density Classifier.
Dynamical systems and online learning
- Carl-Johan Thore:
Implicitly and explicitly constrained optimization problems for training of recurrent neural networks. - Luca Pasa, Alberto Testolin, Alessandro Sperduti:
A HMM-based pre-training approach for sequential data. - Sigurd Spieckermann, Siegmund Düll, Steffen Udluft, Alexander Hentschel, Thomas A. Runkler:
Exploiting similarity in system identification tasks with recurrent neural networks. - Zhemin Zhu, Djoerd Hiemstra, Peter M. G. Apers, Andreas Wombacher:
Comparison of local and global undirected graphical models. - Jialin Liu, Olivier Teytaud:
Meta Online Learning: Experiments on a Unit Commitment Problem. - Hamid Bouchachia, Emili Balaguer-Ballester:
DELA: A Dynamic Online Ensemble Learning Algorithm.
Advances on Weightless Neural Systems
- Massimo De Gregorio, Felipe M. G. França, Priscila M. V. Lima, Wilson Rosa de Oliveira:
Advances on Weightless Neural Systems. - Igor Aleksander, Helen Morton:
Learning state prediction using a weightless neural explorer. - Mariacarla Staffa, Massimo De Gregorio, Maurizio Giordano, Silvia Rossi:
Can you follow that guy? - Douglas de O. Cardoso, Danilo S. Carvalho, Daniel S. F. Alves, Diego Fonseca Pereira de Souza, Hugo C. C. Carneiro, Carlos Eduardo Pedreira, Priscila M. V. Lima, Felipe M. G. França:
Credit analysis with a clustering RAM-based neural classifier. - Adenilton J. da Silva, Wilson Rosa de Oliveira, Teresa Bernarda Ludermir:
Training a classical weightless neural network in a quantum computer. - Paulo Coutinho, Hugo C. C. Carneiro, Danilo S. Carvalho, Felipe M. G. França:
Extracting rules from DRASiW's "mental images". - Wilson Rosa de Oliveira, Adenilton J. da Silva, Teresa Bernarda Ludermir:
Vector space weightless neural networks. - Rafael Lima de Carvalho, Danilo S. Carvalho, Priscila M. V. Lima, Félix Mora-Camino, Felipe M. G. França:
Online tracking of multiple objects using WiSARD. - Adenilton J. da Silva, Wilson Rosa de Oliveira, Teresa Bernarda Ludermir:
Probabilistic automata simulation with single layer weightless neural networks.
Clustering
- Diego Hernán Peluffo-Ordóñez, Carlos Alzate, Johan A. K. Suykens, Germán Castellanos-Domínguez:
Optimal Data Projection for Kernel Spectral Clustering. - Jochen Kerdels, Gabriele Peters:
Supporting GNG-based clustering with local input space histograms. - Mathieu Senelle, Marco Saerens, François Fouss:
The Sum-over-Forests clustering. - Faicel Chamroukhi, Marius Bartcus, Hervé Glotin:
Bayesian non-parametric parsimonious clustering. - Björn Weghenkel, Laurenz Wiskott:
Learning predictive partitions for continuous feature spaces. - Valmir Macário Filho, Francisco de Assis Tenório de Carvalho:
An adjustable p-exponential clustering algorithm. - Jan Faigl, Peter Vanìk, Miroslav Kulich:
Self-organizing map for determination of goal candidates in mobile robot exploration.
Regression, Forceasting and Extreme Learning Machines
- Leonardo José Silvestre, André Paim Lemos, João Pedro Braga, Antônio de Pádua Braga:
Parameter-free regularization in Extreme Learning Machines with affinity matrices. - Michael Leuenberger, Mikhail F. Kanevski:
Feature selection in environmental data mining combining Simulated Annealing and Extreme Learning Machine. - Lahouari Ghouti:
Mobility Prediction Using Fully-Complex Extreme Learning Machines. - Euler G. Horta, Antônio de Pádua Braga:
An Extreme Learning Approach to Active Learning. - Ananda Freire, Guilherme A. Barreto:
A new model selection approach for the ELM network using metaheuristic optimization. - Joseph Ghafari, Emmanuel Herbert, Stéphane Sénécal, Daniel Migault, Stanislas Francfort, Ting Liu:
Extreme learning machines for Internet traffic classification. - Song Li, Peng Wang, Lalit Goel:
Electric load forecasting using wavelet transform and extreme learning machine. - Helon V. H. Ayala, Luciano Ferreira da Cruz, Leandro dos Santos Coelho, Roberto Zanetti Freire:
Swim velocity profile identification through a Dynamic Self-adaptive Multiobjective Harmonic Search and RBF neural networks. - Kaushala Dias, Terry Windeatt:
Dynamic ensemble selection and instantaneous pruning for regression. - Samir Azrour, Sébastien Piérard, Pierre Geurts, Marc Van Droogenbroeck:
Data normalization and supervised learning to assess the condition of patients with multiple sclerosis based on gait analysis. - Alberto Torres, David Díaz, José R. Dorronsoro:
Sparse one hidden layer MLPs. - Konsta Sirvio, Jaakko Hollmén:
Multi-Step Ahead Forecasting of Road Condition Using Least Squares Support Vector Regression.
Label noise in classification
- Benoît Frénay, Ata Kabán:
A comprehensive introduction to label noise. - Maryam Sabzevari, Gonzalo Martínez-Muñoz, Alberto Suárez:
Improving the Robustness of Bagging with Reduced Sampling Size. - Carlos Javier Mantas, Joaquín Abellán:
Credal decision trees in noisy domains. - Anton Akusok, David Veganzones, Yoan Miché, Eric Séverin, Amaury Lendasse:
Finding Originally Mislabels with MD-ELM. - Caroline König, Alfredo Vellido Alcacena, René Alquézar Mancho, Jesús Giraldo:
Misclassification of class C G-protein-coupled receptors as a label noise problem. - Diego Hernán Peluffo-Ordóñez, Santiago Murillo Rendón, Julián D. Arias-Londoño, Germán Castellanos-Domínguez:
A multi-class extension for multi-labeler support vector machines.
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