<TitleType>01</TitleType> <TitleText textcase="01">ESANN 2017 - Proceedings</TitleText> <Subtitle textcase="01">25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning</Subtitle>

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COM.ONIXSUITE.9782875870407 03 02 i6doc 01 SKU 94793PDF 02 2875870408 03 9782875870407 15 9782875870407 DG 1 002 <TitleType>01</TitleType> <TitleText textcase="01">ESANN 2017 - Proceedings</TitleText> <Subtitle textcase="01">25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning</Subtitle> 01 GCOI 28001100477480 1 A32 04 1 01 eng 646 00 646 03 TEC000000 T 29 2012 3069 TECHNIQUES ET SCIENCES APPLIQUEES 01 06 01 Since 1993, ESANN has become a reference for researchers on fundamental and theoretical aspects of artificial neural networks, computational intelligence, machine learning and related topics. Each year, around 120 specialists attend ESANN, in order to present their latest results and comprehensive surveys, and to discuss the future developments in this field. The ESANN 2017 conference follows this tradition, while adapting its scope to the new developments in the field. The ESANN conferences cover artificial neural networks, machine learning, statistical information processing and computational intelligence. Mathematical foundations, algorithms and tools, and applications are covered. The twenty-fifth ESANN will be organised in Bruges, on 26-28 April 2017. It has become a tradition to hold the conference in this beautiful, human-size mediaeval city, whose atmosphere is favourable to efficient work but also to enjoyable cultural visits and relaxation. The centre of Bruges is a UNESCO World Heritage site. 03 Since 1993, ESANN has become a reference for researchers on fundamental and theoretical aspects of artificial neural networks, computational intelligence, machine learning and related topics. Each year, around 120 specialists attend ESANN, in order to present their latest results and comprehensive surveys, and to discuss the future developments in this field. The ESANN 2017 conference follows this tradition, while adapting its scope to the new developments in the field. The ESANN conferences cover artificial neural networks, machine learning, statistical information processing and computational intelligence. Mathematical foundations, algorithms and tools, and applications are covered. The twenty-fifth ESANN will be organised in Bruges, on 26-28 April 2017. It has become a tradition to hold the conference in this beautiful, human-size mediaeval city, whose atmosphere is favourable to efficient work but also to enjoyable cultural visits and relaxation. The centre of Bruges is a UNESCO World Heritage site. 02 The ESANN 2017 conference follows this tradition, while adapting its scope to the new developments in the field. The ESANN conferences cover artificial neural networks, machine learning, statistical information processing and computational intelligence. Mathematical foundations, algorithms and tools, and applications are covered. 04 Deep and kernel methods: best of two worlds Bridging deep and kernel methods L. Belanche, M. Costa-jussa Structure optimization for deep multimodal fusion networks using graph-induced kernels D. Ramachandram, M. Lisicki, T. J. Shields, M. R. Amer, G. W. Taylor Scalable Hybrid Deep Neural Kernel Networks S. Mehrkanoon, A. Zell, J. A. K. Suykens Learning dot-product polynomials for multiclass problems I. Lauriola, M. Donini, F. Aiolli Support vector components analysis M. van der Ree, J. Roerdink, C. Phillips, G. Garraux, E. Salmon, M. Wiering Algebraic multigrid support vector machines E. Sadrfaridpour, S. Jeereddy, K. Kennedy, A. Luckow, T. Razzaghi, I. Safro Attention-based Information Fusion using Multi-Encoder-Decoder Recurrent Neural Networks S. Baier, S. Spieckermann, V. Tresp Fusion of Stereo Vision for Pedestrian Recognition using Convolutional Neural Networks D. O. Pop, A. Rogozan, F. Nashashibi, A. Bensrhair Training convolutional networks with weight–wise adaptive learning rates A. Mosca, G. Magoulas Invariant representations of images for better learning M. M. Issakkimuthu, S. K. V Feature Extraction for On-Road Vehicle Detection Based on Support Vector Machine S. G. Silva Filho, R. Freire, L. d. S. Coelho Predicting Time Series with Space-Time Convolutional and Recurrent Neural Networks W. Groß, S. Lange, J. Bödecker, M. Blum Randomized Machine Learning approaches: analysis and developments Randomized Machine Learning Approaches: Recent Developments and Challenges C. Gallicchio, J. D. Martín-Guerrero, A. Micheli, E. Soria-Olivas Fisher memory of linear Wigner echo state networks P. Tino Generalization Performances of Randomized Classifiers and Algorithms built on Data Dependent Distributions L. Oneto, S. Ridella, D. Anguita ELM Preference Learning for Physiological Data D. Bacciu, M. Colombo, D. Morelli, D. Plans Advanced query strategies for Active Learning with Extreme Learning Machines A. Akusok, E. Eirola, Y. Miche, A. Gritsenko, A. Lendasse Random projection initialization for deep neural networks P. IW Wójcik, M. Kurdziel Classification Fine-grained event learning of human-object interaction with LSTM-CRF T. Do, J. Pustejovsky Distance metric learning: a two-phase approach B. Nguyen, C. Morell, B. De Baets An EM transfer learning algorithm with applications in bionic hand prostheses B. Paassen, A. Schulz, J. Hahne, B. Hammer Dropout Prediction at University of Genoa: a Privacy Preserving Data Driven Approach L. Oneto, A. Siri, G. Luria, D. Anguita Physical activity recognition from sub-bandage sensors using both feature selection and extraction E. D'Andrea, F. Di Francesco, V. Dini, B. Lazzerini, M. Romanelli, P. Salvo A multi-criteria meta-learning method to select under-sampling algorithms for imbalanced datasets R. Morais, P. Miranda, R. Silva Large-scale nonlinear dimensionality reduction for network intrusion detection Y. Hamid, L. Journaux, J. A. Lee, L. Sautot, N. Bushra, M. Sugumaran Acceleration of Prototype Based Models with Cascade Computation C. Karaoguz, A. Gepperth Automatic crime report classi cation through a weightless neural network R. Adnet Pinho, W. Brito, C. Motta, . Lima Efficient Neural-based patent document segmentation with Term Order Probabilities D. Silva de Carvalho, M.-L. Nguyen Biomedical data analysis in translational research: integration of expert knowledge and interpretable models Biomedical data analysis in translational research: integration of expert knowledge and interpretable models G. Bhanot, M. Biehl, T. Villmann, D. Zühlke Feature Relevance Bounds for Linear Classification C. Göpfert, L. Pfannschmidt, B. Hammer Prediction of preterm infant mortality with Gaussian process classification O.-P. Rinta-Koski, S. Särkkä, J. Hollmén, M. Leskinen, S. Andersson Comparison of strategies to learn from imbalanced classes for computer aided diagnosis of inborn steroidogenic disorders S. Ghosh, E. S. Baranowski, R. van Veen, G.-J. de Vries, M. Biehl, W. Arlt, P. Tino, K. Bunte Environmental signal processing: new trends and applications Environmental signal processing: new trends and applications M. Puigt, G. Delmaire, G. Roussel Solving Inverse Source Problems for Sources with Arbitrary Shapes using Sensor Networks J. Murray-Bruce, P. L. Dragotti Non-negative decomposition of geophysical dynamics M. Lopez-Radcenco, A. Aïssa-El-Bey, P. Ailliot, R. Fablet Impact of the initialisation of a blind unmixing method dealing with intra-class variability C. Revel, Y. Deville, V. Achard, X. Briottet Application of Tensor and Matrix Completion on Environmental Sensing Data M. Giannopoulos, S. Savvaki, G. Tsagkatakis, P. Tsakalides Indoor air pollutant sources using Blind Source Separation Methods R. Ouaret, A. Ionescu, O. Ramalho, Y. Candau High dimensionality voltammetric biosensor data processed with artificial neural networks A. González-Calabuig, G. Faura, M. del Valle Kernels, graphs and clustering Learning sparse models of diffusive graph signals S. Dong, D. Thanou, P.-A. Absil, P. Frossard The Conjunctive Disjunctive Node Kernel D. Tran Van, A. Sperduti, F. Costa POKer: a Partial Order Kernel for Comparing Strings with Alternative Substrings M. Abdollahyan, F. Smeraldi Accelerating stochastic kernel SOM J. Mariette, F. Rossi, M. Olteanu, N. Villa-Vialaneix Viral initialization for spectral clustering V. Petrosyan, A. Proutiere Approximated Neighbours MinHash Graph Node Kernel N. Navarin, A. Sperduti Fast hyperparameter selection for graph kernels via subsampling and multiple kernel learning M. Donini, N. Navarin, I. Lauriola, F. Aiolli, F. Costa A Simple Cluster Validation Index with Maximal Coverage S. Jauhiainen, T. Karkkainen The Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-Study P. Glauner, M. Du, V. Paraschiv, A. Boytsov, I. Lopez Andrade, J. A. Meira, P. Valtchev, R. State Regression, robots and biological systems Piecewise-Bézier C1 smoothing on manifolds with application to wind field estimation P.-Y. Gousenbourger, E. Massart, A. Musolas, P.-A. Absil, J. M. Hendrickx, L. Jacques, Y. Marzouk Reducing variance due to importance weighting in covariate shift bias correction V.-T. Tran, A. Aussem Complex activity patterns generated by short-term synaptic plasticity B. Sandor, C. Gros Criticality in Biocomputation T. olde Scheper Scholar Performance Prediction using Boosted Regression Trees Techniques B. Stearns, F. Rangel, F. Rangel, F. Faria, J. Oliveira Imitation learning for a continuum trunk robot M. Malekzadeh Shafaroudi, J. F. Queißer, J. J. Steil ELM vs. WiSARD: a performance comparison L. Oliveira, F. França A novel principle for causal inference in data with small error variance P. Blöbaum, S. Shimizu, T. Washio Learning null space projections fast J. Manavalan, M. Howard Comparison of adaptive MCMC methods E. Milgo, N. Ronoh, P. W. Wagacha, B. Manderick Pseudo-analytical solutions for stochastic options pricing using Monte Carlo simulation and Breeding PSO-trained neural networks S. Palmer, D. Gorse Spikes as regularizers A. Søgaard Moving Least Squares Support Vector Machines for weather temperature prediction Z. Karevan, Y. Feng, J. A. K. Suykens A Robust Minimal Learning Machine based on the M-Estimator J. Gomes, D. Mesquita, A. Freire, A. Souza Junior, T. Karkkainen Processing, Mining and Visualizing Massive Urban Data Processing, mining and visualizing massive urban data P. Borgnat, E. Côme, L. Oukhellou Anomaly detection and characterization in smart card logs using NMF and Tweets E. Tonnelier, N. Baskiotis, V. Guigue, P. Gallinari Using degree constrained gravity null-models to understand the structure of journeys' networks in bicycle sharing systems R. Cazabet, P. Borgnat, P. Jensen A neuro-symbolic approach to GPS trajectory classification D. Carvalho, F. França, R. Barbosa, D. Cardoso Non-negative matrix factorization as a pre-processing tool for travelers temporal profiles clustering L. Carel, P. Alquier Extracting urban water usage habits from smart meter data: a functional clustering approach N. Cheifetz, A. Same, Z. Sabir, A.-C. Sandraz, C. Féliers Multiscale Spatio-Temporal Data Aggregation and Mapping for Urban Data Exploration A. Remy, E. Côme Detection of non-recurrent road traffic events based on clustering indicators P.-A. Laharotte, R. Billot, N.-E. El Faouzi Signal and image processing, collaborative filtering Collaborative filtering with neural networks J. Feigl, M. Bogdan Investigating optical transmission error correction using wavelet transforms W. Binjumah, A. Redyuk, R. Adams, N. Davey, Y. Sun WiSARDrp for Change Detection in Video Sequences M. De Gregorio, G. Maurizio Learning human behaviors and lifestyle by capturing temporal relations in mobility patterns E. Ben Zion, B. Lerner Hierarchical Combination of Video Features for Personalised Pain Level Recognition P. Thiam, V. Kessler, F. Schwenker A performance acceleration algorithm of spectral unmixing via subset selection J. Ke, Y. Guo, A. Sowmya, T. Bednarz Myoelectrical signal classification based on S transform and two-directional 2DPCA H.-B. Xie, H. Liu Hyper-spectral frequency selection for the classification of vegetation diseases K. Dijkstra, J. van de Loosdrecht, L. Schomaker, M. Wiering Outlining a simple and robust method for the automatic detection of EEG arousals I. Fernández-Varela, D. Álvarez-Estévez, E. Hernández-Pereira, V. Moret-Bonillo A decision support system based on cellular automata to help the control of late blight in tomato cultures G. Vianna, G. Oliveira, G. Cunha Comparison of manual and semi-manual delineations for classifying glioblastoma multiforme patients based on histogram and texture MRI features A. Ion-Margineanu, S. Van Cauter, D. M Sima, F. Maes, S. Sunaert, U. Himmelreich, S. Van Huffel Latent variable analysis in hospital electric power demand using non-negative matrix factorization D. García, I. Díaz, D. Pérez, A. Cuadrado, M. Domínguez Supporting generative models of spatial behavior by user interaction R. Hug, W. Hübner, M. Arens Algorithmic Challenges in Big Data Analytics Algorithmic challenges in big data analytics V. Bolón-Canedo, B. Remeseiro, K. Sechidis, D. Martínez-Rego, A. Alonso-Betanzos Partition-wise Recurrent Neural Networks for Point-based AIS Trajectory Classification X. Jiang, E. N. de Souza, X. Liu, B. H. Soleimani, X. Wang, D. L. Silver, S. Matwin Scalable approximate k-NN Graph construction based on Locality Sensitive Hashing C. Eiras-Franco, L. Kanthan, A. Alonso-Betanzos, D. Martínez-Rego Degrees of Freedom in Regression Ensembles R. Henry, G. Brown Mutual information for improving the efficiency of the SCH algorithm D. Fernandez-Francos, O. Fontenla-Romero, A. Alonso-Betanzos, G. Brown . A distributed approach for classification using distance metrics L. Morán-Fernández, V. Bolón-Canedo, A. Alonso-Betanzos Deep learning Local Lyapunov Exponents of Deep RNN C. Gallicchio, A. Micheli, L. Silvestri Learning Semantic Prediction using Pretrained Deep Feedforward Networks J. Wagner, V. Fischer, M. Herman, S. Behnke Deep convolutional neural networks for detecting noisy neighbours in cloud infrastructure B. Ordozgoiti, A. Mozo, S. Gómez Canaval, U. Margolin, E. Rosensweig, I. Segall Real-time convolutional networks for sonar image classification in low-power embedded systems M. Valdenegro-Toro Approximate operations in Convolutional Neural Networks with RNS data representation V. Arrigoni, B. Rossi, P. Fragneto, G. Desoli Learning convolutional neural network to maximize Pos@Top performancemeasure Y. Geng, L. Ru-Ze, W. Li, J. Wang, L. Gaoyuan, X. Chenhao, W. Jing-Yan Active learning strategy for CNN combining batchwise Dropout and Query-By-Committee M. Ducoffe, F. Precioso A Deep Q-Learning Agent for L-Game with Variable Batch Training P. Giannakopoulos, Y. Cotronis TimeNet: Pre-trained deep recurrent neural network for time series classification P. Malhotra, V. TV, L. Vig, P. Agarwal, G. Shroff Uncertain photometric redshifts via combining deep convolutional and mixture density networks A. D'Isanto, K. L. Polsterer Feature Extraction and Learning for RSSI based Indoor Device Localization S. Timotheatos, G. Tsagkatakis, P. Tsakalides, P. Trahanias Author index Committees 06 03 01 https://i6doc.com/resources/titles/28001100477480/images/fc0de4e0396fff257ea362983c2dda5a/HIGHQ/9782875870407.jpg 20170321 07 03 01 https://i6doc.com/resources/titles/28001100477480/images/fc0de4e0396fff257ea362983c2dda5a/THUMBNAIL/9782875870407.jpg 20170321 38 https://i6doc.com/en/book/?GCOI=28001100477480 ESANN 01 ESANN 04 20170321 2017 02 WORLD