Electronic proceedings author index

A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z
Yixuan Sun
  • ESANN 2023 - Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization? [Details]
Lijun Sun
  • ESANN 2021 - Machine learning and data mining for urban mobility intelligence [Details]
Jigang Sun
  • ESANN 2010 - Curvilinear component analysis and Bregman divergences [Details]
Lisheng Sun-Hosoya
  • ESANN 2025 - Encoding Matters: Impact of Categorical Variable Encoding on Performance and Bias [Details]
Stefaan Sunaert
  • ESANN 2017 - Comparison of manual and semi-manual delineations for classifying glioblastoma multiforme patients based on histogram and texture MRI features [Details]
Zachary Susskind
  • No papers found
Zachary Susskind
  • ESANN 2022 - A WiSARD-based conditional branch predictor [Details]
  • ESANN 2022 - Distributive Thermometer: A New Unary Encoding for Weightless Neural Networks [Details]
  • ESANN 2022 - Pruning Weightless Neural Networks [Details]
Shaon Sutradhar
  • ESANN 2019 - Blind-spot network for image anomaly detection: A new approach to diabetic retinopathy screening [Details]
Thorsten Suttorp
  • ESANN 2008 - Approximation of Gaussian process regression models after training [Details]
Easter Selvan Suviseshamuthu
  • ESANN 2012 - Range-based non-orthogonal ICA using cross-entropy method [Details]
J.A.K. Suykens
  • ESANN 2000 - Sparse least squares Support Vector Machine classifiers [Details]
  • ESANN 2000 - The K.U.Leuven competition data: a challenge for advanced neural network techniques [Details]
  • ESANN 2001 - Automatic relevance determination for Least Squares Support Vector Machines classifiers [Details]
  • ESANN 2002 - Prediction of mental development of preterm newborns at birth time using LS-SVM [Details]
  • ESANN 2002 - The use of LS-SVM in the classification of brain tumors based on Magnetic Resonance Spectroscopy signals [Details]
  • ESANN 2003 - Kernel PLS variants for regression [Details]
J.A.K. Suykens
  • ESANN 2011 - Sparse LS-SVMs with L0–norm minimization [Details]
  • ESANN 2011 - Symbolic computing of LS-SVM based models [Details]
Johan A.K. Suykens
  • ESANN 2004 - sparse LS-SVMs using additive regularization with a penalized validation criterion [Details]
Johan A. K. Suykens
  • ESANN 2016 - Clustering from two data sources using a kernel-based approach with weight coupling [Details]
  • ESANN 2016 - Fast in-memory spectral clustering using a fixed-size approach [Details]
  • ESANN 2016 - Spatio-temporal feature selection for black-box weather forecasting [Details]
  • ESANN 2017 - Moving Least Squares Support Vector Machines for weather temperature prediction [Details]
  • ESANN 2017 - Scalable Hybrid Deep Neural Kernel Networks [Details]
J. Suykens
  • ESANN 1995 - NLq theory: unifications in the theory of neural networks, systems and control [Details]
  • ESANN 1998 - Improved generalization ability of neurocontrollers by imposing NLq stability constraints [Details]
Johan Suykens
  • ESANN 2020 - Learning from partially labeled data [Details]
  • ESANN 2022 - Recurrent Restricted Kernel Machines for Time-series Forecasting [Details]
  • ESANN 2024 - Feature Learning using Multi-view Kernel Partial Least Squares [Details]
  • ESANN 2025 - Generative Kernel Spectral Clustering [Details]
Johan Suykens
  • ESANN 2007 - Convex optimization for the design of learning machines [Details]
  • ESANN 2008 - Survival SVM: a practical scalable algorithm [Details]
  • ESANN 2009 - Transductively Learning from Positive Examples Only [Details]
  • ESANN 2010 - Highly sparse kernel spectral clustering with predictive out-of-sample extensions [Details]
  • ESANN 2010 - On the use of a clinical kernel in survival analysis [Details]
  • ESANN 2012 - Interval coded scoring systems for survival analysis [Details]
  • ESANN 2012 - Joint Regression and Linear Combination of Time Series for Optimal Prediction [Details]
  • ESANN 2015 - Ranking Overlap and Outlier Points in Data using Soft Kernel Spectral Clustering [Details]
  • ESANN 2018 - Generative Kernel PCA [Details]
  • ESANN 2018 - Shallow and Deep Models for Domain Adaptation problems [Details]
Yoko Suzuki
  • ESANN 2025 - Sleep Staging with Gradient Boosting and DWT-PSD Features from EEG/EOG Signals [Details]
Markus Svensén
  • ESANN 2004 - Robust Bayesian Mixture Modelling [Details]
Phillip Swazinna
  • ESANN 2025 - TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning [Details]
Phillip Swazinna
  • ESANN 2021 - Behavior Constraining in Weight Space for Offline Reinforcement Learning [Details]
  • ESANN 2023 - Automatic Trade-off Adaptation in Offline RL [Details]
K. Swingler
  • ESANN 1993 - The filtered associative network [Details]
Kevin Swingler
  • ESANN 2013 - Mixed order associative networks for function approximation, optimisation and sampling [Details]
Paul Swoboda
  • ESANN 2026 - NSA: Neuro-symbolic ARC Challenge [Details]
P. Sykacek
  • ESANN 1997 - Equivalent error bars for neural network classifiers trained by Bayesian inference [Details]
Thiria Sylvie
  • ESANN 2012 - Image reconstruction using an iterative SOM based algorithm [Details]
Marie Szafranski
  • ESANN 2023 - Mixture of stochastic block models for multiview clustering [Details]
Sandor Szedmak
  • ESANN 2006 - Synthesis of maximum margin and multiview learning using unlabeled data [Details]
  • ESANN 2007 - A metamorphosis of Canonical Correlation Analysis into multivariate maximum margin learning [Details]
  • ESANN 2014 - Joint SVM for Accurate and Fast Image Tagging [Details]
  • ESANN 2015 - Learning missing edges via kernels in partially-known graphs [Details]
MARIO SZNAIER
  • No papers found
MARIO SZNAIER
  • ESANN 2022 - Dynamics-aware Representation Learning via Multivariate Time Series Transformers [Details]