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Found 8 papers, 5 papers with code
Optimization meets Machine Learning: An Exact Algorithm for Semi-Supervised Support Vector Machines
For the upper bound, instead, we define a local search exploiting the solution of the SDP relaxation.
Predicting municipalities in financial distress: a machine learning approach enhanced by domain expertise
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.
Supervised Feature Compression based on Counterfactual Analysis
Counterfactual Explanations are becoming a de-facto standard in post-hoc interpretable machine learning.
Global Optimization for Cardinality-constrained Minimum Sum-of-Squares Clustering via Semidefinite Programming
In this paper, we propose a global optimization approach based on the branch-and-cut technique to solve the cardinality-constrained MSSC.
An Exact Algorithm for Semi-supervised Minimum Sum-of-Squares Clustering
The minimum sum-of-squares clustering (MSSC), or k-means type clustering, is traditionally considered an unsupervised learning task.
Improving P300 Speller performance by means of optimization and machine learning
First, we propose a new decision function that aims at improving classification performances in terms of accuracy and Information Transfer Rate both in a no stopping and early stopping environment.
A machine learning approach for forecasting hierarchical time series
In this paper, we propose a machine learning approach for forecasting hierarchical time series.
Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network
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.
