Wednesday 22 April 2015
| 09h00 | Opening | ||
| 09h10 | Prototype-based and weightless models | ||
| 09h10 | Median-LVQ for classification of dissimilarity data based on ROC-optimization | ||
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| 09h30 | Certainty-based prototype insertion/deletion for classification with metric adaptation | ||
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| 09h50 | Learning matrix quantization and variants of relevance learning | ||
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| 10h10 | A WiSARD-based multi-term memory framework for online tracking of objects | ||
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| 10h30 | Memory Transfer in DRASiW?like Systems | ||
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| 10h50 | Prototype-based and weightless models Poster spotlights | ||
| 10h50 | Combining dissimilarity measures for prototype-based classification | ||
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| 10h51 | Coffee break | ||
| 11h10 | Emerging techniques and applications in multi-objective reinforcement learning Organized by Madalina M. Drugan, Bernard Manderick, Ann Nowe (Belgium) | ||
| 11h10 | Multi-objective optimization perspectives on reinforcement learning algorithms using reward vectors | ||
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| 11h30 | Thompson Sampling for Multi-Objective Multi-Armed Bandits Problem | ||
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| 11h50 | Pareto Local Search for MOMDP Planning | ||
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| 12h10 | Emerging techniques and applications in multi-objective reinforcement learning Poster spotlights | ||
| 12h10 | Bernoulli bandits: an empirical comparison | ||
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| 12h11 | Sequence learning and time series | ||
| 12h11 | Learning Recurrent Dynamics using Differential Evolution | ||
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| 12h31 | Sequence learning and time series Poster spotlights | ||
| 12h31 | Comparison of Numerical Models and Statistical Learning for Wind Speed Prediction | ||
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| 12h32 | Solar PV Power Forecasting Using Extreme Learning Machine and Information Fusion | ||
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| 12h33 | Gaussian process modelling of multiple short time series | ||
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| 12h34 | Long Short Term Memory Networks for Anomaly Detection in Time Series | ||
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| 12h35 | Hierarchical, prototype-based clustering of multiple time series with missing values | ||
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| 12h36 | Lunch | ||
| 14h00 | Regression and prediction | ||
| 14h00 | Fast greedy insertion and deletion in sparse Gaussian process regression | ||
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| 14h20 | Using self-organizing maps for regression: the importance of the output function | ||
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| 14h40 | Using the Mean Absolute Percentage Error for Regression Models | ||
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| 15h00 | Survival Analysis with Cox Regression and Random Non-linear Projections | ||
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| 15h20 | Regression and prediction Poster spotlights | ||
| 15h20 | Ensemble Learning with Dynamic Ordered Pruning for Regression | ||
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| 15h21 | Training Multi-Layer Perceptron with Multi-Objective Optimization and Spherical Weights Representation | ||
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| 15h22 | Reducing offline evaluation bias of collaborative filtering | ||
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| 15h23 | A new fuzzy neural system with applications | ||
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| 15h24 | Measuring scoring efficiency through goal expectancy estimation | ||
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| 15h25 | Predicting the profitability of agricultural enterprises in dairy farming | ||
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| 15h26 | The use of RBF neural network to predict building?s corners hygrothermal behavior | ||
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| 15h27 | I see you: on neural networks for indoor geolocation | ||
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| 15h28 | Feature and kernel learning Organized by Veronica Bolon Canedo (Spain), Michele Donini, Fabio Aiolli (Italy) | ||
| 15h28 | Feature and kernel learning | ||
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| 15h48 | Discovering temporally extended features for reinforcement learning in domains with delayed causalities | ||
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| 16h08 | ESNigma: efficient feature selection for echo state networks | ||
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| 16h28 | Feature and kernel learning Poster spotlights | ||
| 16h28 | Learning features on tear film lipid layer classification | ||
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| 16h29 | PCA-based algorithm for feature score measures ensemble construction | ||
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| 16h30 | Coffee break and poster preview | ||
Thursday 23 April 2015
| 09h00 | Graphs in machine learning Organized by Pierre Latouche, Fabrice Rossi (France) | ||
| 09h00 | Graphs in machine learning. An introduction | ||
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| 09h20 | Exploiting the ODD framework to define a novel effective graph kernel | ||
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| 09h40 | Exact ICL maximization in a non-stationary time extension of latent block model for dynamic networks | ||
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| 10h00 | A State-Space Model for the Dynamic Random Subgraph Model | ||
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| 10h20 | Graphs in machine learning Poster spotlights | ||
| 10h20 | Gabriel Graph for Dataset Structure and Large Margin Classification: A Bayesian Approach | ||
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| 10h21 | Coffee break | ||
| 10h40 | Manifold learning and optimization | ||
| 10h40 | Supervised Manifold Learning with Incremental Stochastic Embeddings | ||
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| 11h00 | Rank-constrained optimization: a Riemannian manifold approach | ||
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| 11h20 | Asynchronous decentralized convex optimization through short-term gradient averaging | ||
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| 11h40 | Feature and model selection, sparse models | ||
| 11h40 | Model Selection for Big Data: Algorithmic Stability and Bag of Little Bootstraps on GPUs | ||
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| 12h00 | Solving constrained Lasso and Elastic Net using nu-SVMs | ||
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| 12h20 | Feature and model selection, sparse models Poster spotlights | ||
| 12h20 | Assessment of feature saliency of MLP using analytic sensitivity | ||
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| 12h21 | Morisita-based feature selection for regression problems | ||
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| 12h22 | A new genetic algorithm for multi-label correlation-based feature selection | ||
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| 12h23 | Search Strategies for Binary Feature Selection for a Naive Bayes Classifier | ||
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| 12h24 | Lunch | ||
| 14h00 | Advances in learning analytics and educational data mining Organized by Alessandro Ghio, Luca Oneto, Davide Anguita (Italy), Mathias Funk, Matthias Rauterberg (The Netherlands) | ||
| 14h00 | Advances in learning analytics and educational data mining | ||
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| 14h20 | Adaptive structure metrics for automated feedback provision in Java programming | ||
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| 14h40 | Human Algorithmic Stability and Human Rademacher Complexity | ||
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| 15h00 | High-School Dropout Prediction Using Machine Learning: A Danish Large-scale Study | ||
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| 15h20 | Advances in learning analytics and educational data mining Poster spotlights | ||
| 15h20 | The prediction of learning performance using features of note taking activities | ||
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| 15h21 | Enhancing learning at work. How to combine theoretical and data-driven approaches, and multiple levels of data? | ||
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| 15h22 | Weighted Clustering of Sparse Educational Data | ||
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| 15h23 | Classification | ||
| 15h23 | An affinity matrix approach for structure selection of extreme learning machines | ||
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| 15h43 | A generalised label noise model for classification | ||
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| 16h03 | On the use of machine learning techniques for the analysis of spontaneous reactions in automated hearing assessment | ||
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| 16h23 | Combining higher-order N-grams and intelligent sample selection to improve language modeling for Handwritten Text Recognition | ||
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| 16h43 | Classification Poster spotlights | ||
| 16h43 | Learning Sparse Feature Representations using Probabilistic Quadtrees and Deep Belief Nets | ||
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| 16h44 | Optimal transport for semi-supervised domain adaptation | ||
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| 16h45 | Resource-efficient Incremental learning in very high dimensions | ||
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| 16h46 | One-vs-all binarization technique in the context of random forest | ||
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| 16h47 | Improving the random forest algorithm by randomly varying the size of the bootstrap samples for low dimensional data sets | ||
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| 16h48 | An Ensemble Learning Technique for Multipartite Ranking | ||
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| 16h49 | Online multiclass learning with "bandit" feedback under a Passive-Aggressive approach | ||
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| 16h50 | Data Analytics for Drilling Operational States Classifications | ||
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| 16h51 | Prediction of concrete carbonation depth using decision trees | ||
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| 16h52 | Powered-Two-Wheeler safety critical events recognition using a mixture model with quadratic logistic functions | ||
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| 16h53 | Coffee break and poster preview | ||
Friday 24 April 2015
| 09h00 | Image processing and vision systems | ||
| 09h00 | Real-time activity recognition via deep learning of motion features | ||
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| 09h20 | Designing semantic feature spaces for brain-reading | ||
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| 09h40 | Learning objects from RGB-D sensors using point cloud-based neural networks | ||
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| 10h00 | A Robust Neural Robot Navigation Using a Combination of Deliberative and Reactive Control Architectures | ||
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| 10h20 | Robust Visual Terrain Classification with Recurrent Neural Networks | ||
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| 10h40 | Image processing and vision systems Poster spotlights | ||
| 10h40 | Revisiting ant colony algorithms to seismic faults detection | ||
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| 10h41 | Depth and height aware semantic RGB-D perception with convolutional neural networks | ||
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| 10h42 | A simple technique for improving multi-class classification with neural networks | ||
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| 10h43 | Dynamic gesture recognition using Echo State Networks | ||
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| 10h44 | A flat neural network architecture to represent movement primitives with integrated sequencing | ||
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| 10h45 | Coffee break | ||
| 11h05 | Unsupervised nonlinear dimensionality reduction Organized by John A. Lee (Belgium), Kerstin Bunte (Finland) | ||
| 11h05 | Unsupervised dimensionality reduction: the challenge of big data visualization | ||
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| 11h25 | Autoencoding time series for visualisation | ||
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| 11h45 | Diffusion Maps parameters selection based on neighbourhood preservation | ||
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| 12h05 | Unsupervised nonlinear dimensionality reduction Poster spotlights | ||
| 12h05 | Unsupervised Dimensionality Reduction for Transfer Learning | ||
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| 12h06 | Efficient unsupervised clustering for spatial birds population analysis along the river Loire | ||
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| 12h07 | NLDR methods for high dimensional NIRS dataset : application to vineyard soils characterization | ||
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| 12h08 | Geometrical homotopy for data visualization | ||
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| 12h09 | Lunch | ||
| 13h40 | Unsupervised learning | ||
| 13h40 | On the equivalence between regularized NMF and similarity-augmented graph partitioning | ||
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| 14h00 | Ranking Overlap and Outlier Points in Data using Soft Kernel Spectral Clustering | ||
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| 14h20 | Unsupervised learning Poster spotlights | ||
| 14h20 | Towards a Tomographic Index of Systemic Risk Measures | ||
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| 14h21 | An objective function for self-limiting neural plasticity rules. | ||
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| 14h22 | Kernel methods | ||
| 14h22 | Probabilistic Classification Vector Machine at large scale | ||
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| 14h42 | Online Learning with Operator-valued Kernels | ||
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| 15h02 | Kernel methods Poster spotlights | ||
| 15h02 | Online One-class Classification for Intrusion Detection Based on the Mahalanobis Distance | ||
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| 15h03 | I/S-Race: An iterative Multi-Objective Racing Algorithm for the SVM Parameter Selection Problem | ||
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| 15h04 | SMO Lattices for the Parallel Training of Support Vector Machines | ||
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| 15h05 | Pareto front of bi-objective kernel-based nonnegative matrix factorization | ||
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| 15h06 | Learning missing edges via kernels in partially-known graphs | ||
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| 15h07 | Coffee break and poster preview | ||
| 16h45 | End of conference | ||