Search Results for author: Antonio M. Sudoso

Found 8 papers, 6 papers with code

An SDP-based Branch-and-Cut Algorithm for Biclustering

1 code implementation17 Mar 2024 Antonio M. Sudoso

Biclustering, also called co-clustering, block clustering, or two-way clustering, involves the simultaneous clustering of both the rows and columns of a data matrix into distinct groups, such that the rows and columns within a group display similar patterns.

Clustering valid

Optimizing accuracy and diversity: a multi-task approach to forecast combinations

1 code implementation31 Oct 2023 Giovanni Felici, Antonio M. Sudoso

In essence, it incorporates an additional learning and optimization task into the standard feature-based forecasting approach, focusing on the identification of an optimal set of forecasting methods.

Model Selection Multi-Task Learning +1

Predicting municipalities in financial distress: a machine learning approach enhanced by domain expertise

no code implementations11 Feb 2023 Dario Piermarini, Antonio M. Sudoso, Veronica Piccialli

Predicting financial distress in municipalities can be a complex task, as it involves understanding a wide range of factors that can affect a municipality's financial health.

Global Optimization for Cardinality-constrained Minimum Sum-of-Squares Clustering via Semidefinite Programming

1 code implementation19 Sep 2022 Veronica Piccialli, Antonio M. Sudoso

In this paper, we propose a global optimization approach based on the branch-and-cut technique to solve the cardinality-constrained MSSC.

Clustering

An Exact Algorithm for Semi-supervised Minimum Sum-of-Squares Clustering

1 code implementation30 Nov 2021 Veronica Piccialli, Anna Russo Russo, Antonio M. Sudoso

The minimum sum-of-squares clustering (MSSC), or k-means type clustering, is traditionally considered an unsupervised learning task.

Constrained Clustering

Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network

3 code implementations15 Nov 2019 Veronica Piccialli, Antonio M. Sudoso

Energy disaggregation, known in the literature as Non-Intrusive Load Monitoring (NILM), is the task of inferring the power demand of the individual appliances given the aggregate power demand recorded by a single smart meter which monitors multiple appliances.

Denoising Machine Translation +6

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