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
A. Kanstein
- ESANN 1994 - Self-organizing maps based on differential equations [Details]
- ESANN 2017 - Scalable approximate k-NN Graph construction based on Locality Sensitive Hashing [Details]
- ESANN 2003 - On radial basis function network equalization in the GSM system [Details]
- ESANN 2008 - Word recognition and incremental learning based on neural associative memories and hidden Markov models [Details]
- ESANN 2016 - Maximum likelihood learning of RBMs with Gaussian visible units on the Stiefel manifold [Details]
- ESANN 2017 - Acceleration of Prototype Based Models with Cascade Computation [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 1996 - Neural approaches to independent component analysis and source separation [Details]
- ESANN 2015 - Learning Sparse Feature Representations using Probabilistic Quadtrees and Deep Belief Nets [Details]
- ESANN 2020 - Problem Transformation Methods with Distance-Based Learning for Multi-Target Regression [Details]
- ESANN 2021 - Instance-Based Multi-Label Classification via Multi-Target Distance Regression [Details]
- ESANN 2023 - Feature Selection for Multi-label Classification with Minimal Learning Machine [Details]
- ESANN 2016 - Comparison of Four- and Six-Layered Configurations for Deep Network Pretraining [Details]
- ESANN 2016 - Initialization of big data clustering using distributionally balanced folding [Details]
- ESANN 2016 - Multicriteria optimized MLP for imbalanced learning [Details]
- ESANN 2017 - A Robust Minimal Learning Machine based on the M-Estimator [Details]
- ESANN 2017 - A Simple Cluster Validation Index with Maximal Coverage [Details]
- ESANN 2021 - Orientation Adaptive Minimal Learning Machine for Directions of Atomic Forces [Details]
- ESANN 2014 - Region of interest detection using MLP [Details]
- ESANN 2015 - Assessment of feature saliency of MLP using analytic sensitivity [Details]
- ESANN 2015 - Hierarchical, prototype-based clustering of multiple time series with missing values [Details]
- ESANN 2015 - Weighted Clustering of Sparse Educational Data [Details]
- ESANN 2018 - Comparison of cluster validation indices with missing data [Details]
- ESANN 2018 - Extreme Minimal Learning Machine [Details]
- ESANN 2018 - Scalable robust clustering method for large and sparse data [Details]
- ESANN 2019 - Feature and Algorithm Selection for Capacitated Vehicle Routing Problems [Details]
- ESANN 2019 - Hybrid vibration signal monitoring approach for rolling element bearings [Details]
- ESANN 2019 - Model selection for Extreme Minimal Learning Machine using sampling [Details]
- ESANN 2019 - Sparse minimal learning machine using a diversity measure minimization [Details]
- ESANN 2020 - A Distributed Neural Network Architecture for Robust Non-Linear Spatio-Temporal Prediction [Details]
- ESANN 2006 - LS-SVM functional network for time series prediction [Details]
- ESANN 2007 - Kernel-based online machine learning and support vector reduction [Details]
- ESANN 2016 - Efficient low rank approximation via alternating least squares for scalable kernel learning [Details]
- ESANN 1998 - Polyhedral mixture of linear experts for many-to-one mapping inversion [Details]
- ESANN 1995 - Performance analysis of a MLP weight initialization algorithm [Details]
- ESANN 2020 - On-edge adaptive acoustic models: an application to acoustic person presence detection [Details]
- ESANN 2021 - Real-time On-edge Classification: an Application to Domestic Acoustic Event Recognition [Details]
- ESANN 2022 - Constraint Guided Gradient Descent: Guided Training with Inequality Constraints [Details]
- ESANN 2024 - Constraints as Alternative Learning Objective in Deep Learning [Details]
- ESANN 2007 - Transition from initialization to working stage in biologically realistic networks [Details]
- ESANN 2006 - Generalization properties of spiking neurons trained with ReSuMe method [Details]
- ESANN 2006 - Neural networks and machine learning in bioinformatics - theory and applications [Details]
- ESANN 2006 - Visualizing gene interaction graphs with local multidimensional scaling [Details]
- ESANN 2007 - Functional elements and networks in fMRI [Details]
- ESANN 2012 - Sparse Nonparametric Topic Model for Transfer Learning [Details]
- ESANN 1998 - Methods for interpreting a self-organized map in data analysis [Details]
- ESANN 1995 - Simplified cascade-correlation learning [Details]
- ESANN 1996 - Maximum covariance method for weight initialization of multilayer perceptron network [Details]
- ESANN 1998 - Recurrent SOM with local linear models in time series prediction [Details]
- ESANN 2007 - Bifurcation analysis for a discrete-time Hopfield neural network of two neurons with two delays [Details]
- ESANN 1996 - Recurrent least square learning for quasi-parallel principal component analysis [Details]
- ESANN 2005 - Efficient reinforcement learning through Evolutionary Acquisition of Neural Topologies [Details]
- ESANN 2012 - Integration of Structural Expert Knowledge about Classes for Classification Using the Fuzzy Supervised Neural Gas [Details]
- ESANN 2012 - Modified Conn-Index for the evaluation of fuzzy clusterings [Details]
- ESANN 2013 - A sparse kernelized matrix learning vector quantization model for human activity recognition [Details]
- ESANN 2013 - Border sensitive fuzzy vector quantization in semi-supervised learning [Details]
- ESANN 2013 - Processing Hyperspectral Data in Machine Learning [Details]
- ESANN 2013 - Regularization in relevance learning vector quantization using l1-norms [Details]
- ESANN 2021 - Functional Gradient Descent for n-Tuple Regression [Details]
- ESANN 2019 - Beta Distribution Drift Detection for Adaptive Classifiers [Details]