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Optimal Siting and Sizing of Wayside Energy Storage Systems in a D.C. Railway Line

Energies (Basel), 2020-12, Vol.13 (23), p.6271

2020

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Supervised and unsupervised learning to classify scoliosis and healthy subjects based on non-invasive rasterstereography analysis

PloS one, 2021-12, Vol.16 (12), p.e0261511-e0261511

2021

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Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis

PloS one, 2020-03, Vol.15 (3), p.e0230219-e0230219

2020

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Unboxing Tree ensembles for interpretability: A hierarchical visualization tool and a multivariate optimal re-built tree

EURO journal on computational optimization, 2024, Vol.12, p.100084, Article 100084

2024

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Machine Learning Use for Prognostic Purposes in Multiple Sclerosis

Life (Basel, Switzerland), 2021-02, Vol.11 (2), p.122

2021

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Global optimization issues in deep network regression: an overview

Journal of global optimization, 2019-02, Vol.73 (2), p.239-277

Palagi, Laura

2019

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Data of patients undergoing rehabilitation programs

Data in brief, 2020-06, Vol.30, p.105419-105419, Article 105419

2020

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MOSES: A New Approach to Integrate Interactome Topology and Functional Features for Disease Gene Prediction

Genes, 2021-10, Vol.12 (11), p.1713

2021

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Deep Neural Network Regression to Assist Non-Invasive Diagnosis of Portal Hypertension

Healthcare (Basel), 2023-09, Vol.11 (18), p.2603

2023

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On the convergence of a Block-Coordinate Incremental Gradient method

Soft computing (Berlin, Germany), 2021-10, Vol.25 (19), p.12615-12626

2021

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A neural network approach to the combined multi-objective optimization of the thermodynamic cycle and the radial inflow turbine for Organic Rankine cycle applications

Applied energy, 2019-03, Vol.237, p.210-226

2019

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SpeeDP: an algorithm to compute SDP bounds for very large Max-Cut instances

Mathematical programming, 2012-12, Vol.136 (2), p.353-373

2012

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A MILP methodology to optimize sizing of PV - Wind renewable energy systems

Energy (Oxford), 2018-12, Vol.165, p.385-398

2018

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Branching with hyperplanes in the criterion space: The frontier partitioner algorithm for biobjective integer programming

European journal of operational research, 2020-05, Vol.283 (1), p.57-69

2020

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IN TRIDUO DIE FESTA (PETR. SAT. 45.4): IPOTESI SULLA DATA DELLA CENA DI TRIMALCHIONE

Prometheus (Firenze), 2023-01, Vol.49, p.157-167

Palagi, Laura Bocciolini

2023

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Machine Learning for the prediction of the dynamic behavior of a small scale ORC system

Energy (Oxford), 2019-01, Vol.166, p.72-82

2019

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Margin optimal classification trees

Computers & operations research, 2024-01, Vol.161, p.106441, Article 106441

2024

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A compact formulation for the base station deployment problem in wireless networks

Networks, 2023-07, Vol.82 (1), p.52-67

2023

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LE 'PRETESE' DI UN MAESTRO (NOTA A PETRONIO, SAT. 46.5)

Prometheus (Firenze), 2022-01, Vol.48, p.164-178

Palagi, Laura Bocciolini

2022

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Using SVM to combine global heuristics for the Standard Quadratic Problem

European journal of operational research, 2015-03, Vol.241 (3), p.596-605

2015

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Block layer decomposition schemes for training deep neural networks

Journal of global optimization, 2020-05, Vol.77 (1), p.97-124

2020

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A truncated Newton method in an augmented Lagrangian framework for nonlinear programming

Computational optimization and applications, 2010-03, Vol.45 (2), p.311-352

2010

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Parallel decomposition methods for linearly constrained problems subject to simple bound with application to the SVMs training

Computational optimization and applications, 2018-09, Vol.71 (1), p.115-145

2018

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A distributed bandwidth allocation algorithm for bluetooth wireless networks based on Wardrop equilibrium

2015 34th Chinese Control Conference (CCC), 2015, p.6469-6474

2015

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Tuning parameters of deep neural network training algorithms pays off: a computational study

TOP, 2024-09

2024

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Optimal Siting and Sizing of Wayside Energy Storage Systems in a D.C. Railway Line

Supervised and unsupervised learning to classify scoliosis and healthy subjects based on non-invasive rasterstereography analysis

Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis

Unboxing Tree ensembles for interpretability: A hierarchical visualization tool and a multivariate optimal re-built tree

Machine Learning Use for Prognostic Purposes in Multiple Sclerosis

Global optimization issues in deep network regression: an overview

Data of patients undergoing rehabilitation programs

MOSES: A New Approach to Integrate Interactome Topology and Functional Features for Disease Gene Prediction

Deep Neural Network Regression to Assist Non-Invasive Diagnosis of Portal Hypertension

On the convergence of a Block-Coordinate Incremental Gradient method

A neural network approach to the combined multi-objective optimization of the thermodynamic cycle and the radial inflow turbine for Organic Rankine cycle applications

SpeeDP: an algorithm to compute SDP bounds for very large Max-Cut instances

A MILP methodology to optimize sizing of PV - Wind renewable energy systems

Branching with hyperplanes in the criterion space: The frontier partitioner algorithm for biobjective integer programming

IN TRIDUO DIE FESTA (PETR. SAT. 45.4): IPOTESI SULLA DATA DELLA CENA DI TRIMALCHIONE

Machine Learning for the prediction of the dynamic behavior of a small scale ORC system

Margin optimal classification trees

A compact formulation for the base station deployment problem in wireless networks

LE 'PRETESE' DI UN MAESTRO (NOTA A PETRONIO, SAT. 46.5)

Using SVM to combine global heuristics for the Standard Quadratic Problem

Block layer decomposition schemes for training deep neural networks

A truncated Newton method in an augmented Lagrangian framework for nonlinear programming

Parallel decomposition methods for linearly constrained problems subject to simple bound with application to the SVMs training

A distributed bandwidth allocation algorithm for bluetooth wireless networks based on Wardrop equilibrium

Tuning parameters of deep neural network training algorithms pays off: a computational study

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