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Artificial Neural Networks in Pattern Recognition: 10th IAPR TC3 Workshop, ANNPR 2022, Dubai, United Arab Emirates, November 24-26, 2022, Proceedings

1, 2022

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Artificial Neural Networks in Pattern Recognition: 8th IAPR TC3 Workshop, ANNPR 2018, Siena, Italy, September 19-21, 2018, Proceedings

1st Edition 2018, 2018

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Artificial Neural Networks in Pattern Recognition: 7th IAPR TC3 Workshop, ANNPR 2016, Ulm, Germany, September 28-30, 2016, Proceedings

1st Edition 2016, 2016

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Pattern classification and clustering: A review of partially supervised learning approaches

Pattern recognition letters, 2014-02, Vol.37, p.4-14

2014

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A Neural Probabilistic Graphical Model for Learning and Decision Making in Evolving Structured Environments

Mathematics (Basel), 2022-08, Vol.10 (15), p.2646

Trentin, Edmondo

2022

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Asymptotic Convergence of Soft-Constrained Neural Networks for Density Estimation

Mathematics (Basel), 2020-04, Vol.8 (4), p.572

Trentin, Edmondo

2020

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Downward-Growing Neural Networks

Entropy (Basel, Switzerland), 2023-04, Vol.25 (5), p.733

2023

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Hybrid Random Fields: A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models

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Multivariate Density Estimation with Deep Neural Mixture Models

Neural processing letters, 2023-12, Vol.55 (7), p.9139-9154

Trentin, Edmondo

2023

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Use of pattern recognition and neural networks for non-metric sex diagnosis from lateral shape of calvarium: an innovative model for computer-aided diagnosis in forensic and physical anthropology

International journal of legal medicine, 2017-05, Vol.131 (3), p.823-833

2017

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Soft-Constrained Neural Networks for Nonparametric Density Estimation

Neural processing letters, 2018-10, Vol.48 (2), p.915-932

Trentin, Edmondo

2018

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Networks with trainable amplitude of activation functions

Neural networks, 2001-05, Vol.14 (4), p.471-493

Trentin, Edmondo

2001

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Maximum-likelihood normalization of features increases the robustness of neural-based spoken human-computer interaction

Pattern recognition letters, 2015-11, Vol.66, p.71-80

Trentin, Edmondo

2015

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Emotion recognition from speech signals via a probabilistic echo-state network

Pattern recognition letters, 2015-11, Vol.66, p.4-12

2015

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Combination of supervised and unsupervised learning for training the activation functions of neural networks

Pattern recognition letters, 2014-02, Vol.37, p.178-191

2014

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Recursive Neural Networks for Density Estimation Over Generalized Random Graphs

IEEE transaction on neural networks and learning systems, 2018-11, Vol.29 (11), p.5441-5458

2018

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IEEE_Xplore

Nonparametric small random networks for graph-structured pattern recognition

Neurocomputing (Amsterdam), 2018-11, Vol.313, p.14-24

2018

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Partially Supervised Learning: First IAPR TC3 Workshop, PSL 2011, Ulm, Germany, September 15-16, 2011, Revised Selected Papers

1, 2012

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Maximum-Likelihood Estimation of Neural Mixture Densities: Model, Algorithm, and Preliminary Experimental Evaluation

Artificial Neural Networks in Pattern Recognition, p.178-189

Trentin, Edmondo

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Partially supervised learning for pattern recognition

Pattern recognition letters, 2014-02, Vol.37, p.1-3

2014

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Neural-based downlink scheduling algorithm for broadband wireless networks

Computer communications, , Vol.30 (2), p.207-218

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Soft-Constrained Nonparametric Density Estimation with Artificial Neural Networks

Artificial Neural Networks in Pattern Recognition, p.68-79

Trentin, Edmondo

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A Novel Representation of Graphical Patterns for Graph Convolution Networks

Artificial Neural Networks in Pattern Recognition, p.16-27

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Hybrid Random Fields: A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models (Intelligent Systems Reference Library, Vol 15)

1. Aufl., 2011

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A Refinement Algorithm for Deep Learning via Error-Driven Propagation of Target Outputs

Artificial Neural Networks in Pattern Recognition, p.78-89

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Artificial Neural Networks in Pattern Recognition: 10th IAPR TC3 Workshop, ANNPR 2022, Dubai, United Arab Emirates, November 24-26, 2022, Proceedings

Artificial Neural Networks in Pattern Recognition: 8th IAPR TC3 Workshop, ANNPR 2018, Siena, Italy, September 19-21, 2018, Proceedings

Artificial Neural Networks in Pattern Recognition: 7th IAPR TC3 Workshop, ANNPR 2016, Ulm, Germany, September 28-30, 2016, Proceedings

Pattern classification and clustering: A review of partially supervised learning approaches

A Neural Probabilistic Graphical Model for Learning and Decision Making in Evolving Structured Environments

Asymptotic Convergence of Soft-Constrained Neural Networks for Density Estimation

Downward-Growing Neural Networks

Hybrid Random Fields: A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models

Multivariate Density Estimation with Deep Neural Mixture Models

Use of pattern recognition and neural networks for non-metric sex diagnosis from lateral shape of calvarium: an innovative model for computer-aided diagnosis in forensic and physical anthropology

Soft-Constrained Neural Networks for Nonparametric Density Estimation

Networks with trainable amplitude of activation functions

Maximum-likelihood normalization of features increases the robustness of neural-based spoken human-computer interaction

Emotion recognition from speech signals via a probabilistic echo-state network

Combination of supervised and unsupervised learning for training the activation functions of neural networks

Recursive Neural Networks for Density Estimation Over Generalized Random Graphs

Nonparametric small random networks for graph-structured pattern recognition

Partially Supervised Learning: First IAPR TC3 Workshop, PSL 2011, Ulm, Germany, September 15-16, 2011, Revised Selected Papers

Maximum-Likelihood Estimation of Neural Mixture Densities: Model, Algorithm, and Preliminary Experimental Evaluation

Partially supervised learning for pattern recognition

Neural-based downlink scheduling algorithm for broadband wireless networks

Soft-Constrained Nonparametric Density Estimation with Artificial Neural Networks

A Novel Representation of Graphical Patterns for Graph Convolution Networks

Hybrid Random Fields: A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models (Intelligent Systems Reference Library, Vol 15)

A Refinement Algorithm for Deep Learning via Error-Driven Propagation of Target Outputs

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