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José P. Amorim
- ESANN 2018 - Interpreting deep learning models for ordinal problems [Details]
- ESANN 2018 - Order Crossover for the Inventory Routing Problem [Details]
- ESANN 2007 - Transition from initialization to working stage in biologically realistic networks [Details]
- ESANN 2004 - A New Learning Rates Adaptation Strategy for the Resilient Propagation Algorithm [Details]
- ESANN 1997 - Sequential hypotheses tests for modelling neural networks [Details]
- ESANN 2019 - Deep RL for autonomous robots: limitations and safety challenges [Details]
- ESANN 2017 - Prediction of preterm infant mortality with Gaussian process classification [Details]
- ESANN 2004 - Convergence properties of a fuzzy ARTMAP network [Details]
- ESANN 2004 - An informational energy LVQ approach for feature ranking [Details]
- ESANN 2004 - Neural networks for data mining: constrains and open problems [Details]
- ESANN 2003 - A Fuzzy ARTMAP Probability Estimator with Relevance Factor [Details]
- ESANN 2018 - Dynamic autonomous image segmentation based on Grow Cut [Details]
- ESANN 2020 - Graph Neural Networks for the Prediction of Protein-Protein Interfaces [Details]
- ESANN 2022 - A Deep Learning approach for oocytes segmentation and analysis [Details]
- ESANN 2022 - Deep Semantic Segmentation Models in Computer Vision [Details]
- ESANN 1995 - Improvement of EEG classification with a subject-specific feature selection [Details]
- ESANN 1995 - Trimming the inputs of RBF networks [Details]
- ESANN 2024 - HDBSCAN for 3-rd order tensor [Details]
- ESANN 2023 - Language Modeling in Logistics: Customer Calling Prediction [Details]
- ESANN 2007 - Causality and communities in neural networks [Details]
- ESANN 2024 - Unsupervised Drift Detection Using Quadtree Spatial Mapping [Details]
- ESANN 2020 - On Learning a Control System without Continuous Feedback [Details]
- ESANN 2016 - Challenges in Deep Learning [Details]
- ESANN 2018 - interpretation of convolutional neural networks for speech regression from electrocorticography [Details]
- ESANN 2007 - Interval discriminant analysis using support vector machines [Details]
- ESANN 2010 - Maximal Discrepancy for Support Vector Machines [Details]
- ESANN 2011 - Maximal Discrepancy vs. Rademacher Complexity for error estimation [Details]
- ESANN 2012 - Structural Risk Minimization and Rademacher Complexity for Regression [Details]
- ESANN 2012 - The `K' in K-fold Cross Validation [Details]
- ESANN 2013 - A Learning Machine with a Bit-Based Hypothesis Space [Details]
- ESANN 2013 - A Public Domain Dataset for Human Activity Recognition using Smartphones [Details]
- ESANN 2013 - Human Activity and Motion Disorder Recognition: towards smarter Interactive Cognitive Environments [Details]
- ESANN 2014 - Learning with few bits on small-scale devices: From regularization to energy efficiency [Details]
- ESANN 2015 - Advances in learning analytics and educational data mining [Details]
- ESANN 2015 - Human Algorithmic Stability and Human Rademacher Complexity [Details]
- ESANN 2015 - Model Selection for Big Data: Algorithmic Stability and Bag of Little Bootstraps on GPUs [Details]
- ESANN 2016 - Advances in Learning with Kernels: Theory and Practice in a World of growing Constraints [Details]
- ESANN 2016 - Measuring the Expressivity of Graph Kernels through the Rademacher Complexity [Details]
- ESANN 2016 - Random Forests Model Selection [Details]
- ESANN 2016 - Tuning the Distribution Dependent Prior in the PAC-Bayes Framework based on Empirical Data [Details]
- ESANN 2017 - Dropout Prediction at University of Genoa: a Privacy Preserving Data Driven Approach [Details]
- ESANN 2017 - Generalization Performances of Randomized Classifiers and Algorithms built on Data Dependent Distributions [Details]
- ESANN 2018 - Emerging trends in machine learning: beyond conventional methods and data [Details]
- ESANN 2018 - Local Rademacher Complexity Machine [Details]
- ESANN 2001 - Perspectives on dedicated hardware implementations [Details]
- ESANN 1998 - What are the main factors involved in the design of a Radial Basis Function Network? [Details]
- ESANN 2020 - Improving the Union Bound: a Distribution Dependent Approach [Details]
- ESANN 2021 - In-Station Train Movements Prediction: from Shallow to Deep Multi Scale Models [Details]
- ESANN 2021 - The Benefits of Adversarial Defence in Generalisation [Details]
- ESANN 2022 - Do We Really Need a New Theory to Understand the Double-Descent? [Details]
- ESANN 2022 - Simple Non Regressive Informed Machine Learning Model for Predictive Maintenance of Railway Critical Assets [Details]
- ESANN 2023 - Mitigating Robustness Bias: Theoretical Results and Empirical Evidences [Details]
- ESANN 2023 - Towards Randomized Algorithms and Models that We Can Trust: a Theoretical Perspective [Details]
- ESANN 2024 - Informed Machine Learning for Complex Data [Details]
- ESANN 2024 - Informed Machine Learning: Excess Risk and Generalization [Details]
- ESANN 2007 - Interval discriminant analysis using support vector machines [Details]
- ESANN 2009 - SVM-based learning method for improving colour adjustment in automotive basecoat manufacturing [Details]
- ESANN 2011 - A post-processing strategy for SVM learning from unbalanced data [Details]
- ESANN 1998 - A Tikhonov approach to calculate regularisation matrices [Details]
- ESANN 2002 - An unified framework for 'All data at once' multi-class Support Vector Machines [Details]
- ESANN 2002 - Rule extraction from support vector machines [Details]
- ESANN 2003 - 1-v-1 Tri-Class SV Machine [Details]
- ESANN 1999 - Dimensionality reduction by local processing [Details]
- ESANN 1999 - Segmentation-free detection of overtaking vehicles with a two-stage time-delay neural network classifier [Details]
- ESANN 1997 - Probabilistic self organized map - application to classification [Details]
- ESANN 2024 - Hyperbolic Metabolite-Disease Association Prediction [Details]