0000000000674608

AUTHOR

João P. P. Gomes

showing 3 related works from this author

OnMLM: An Online Formulation for the Minimal Learning Machine

2019

Minimal Learning Machine (MLM) is a nonlinear learning algorithm designed to work on both classification and regression tasks. In its original formulation, MLM builds a linear mapping between distance matrices in the input and output spaces using the Ordinary Least Squares (OLS) algorithm. Although the OLS algorithm is a very efficient choice, when it comes to applications in big data and streams of data, online learning is more scalable and thus applicable. In that regard, our objective of this work is to propose an online version of the MLM. The Online Minimal Learning Machine (OnMLM), a new MLM-based formulation capable of online and incremental learning. The achievements of OnMLM in our…

Minimal Learning MachineComputer scienceonline learning02 engineering and technology010501 environmental sciencesMachine learningcomputer.software_genre01 natural sciencesbig data0202 electrical engineering electronic engineering information engineeringstokastiset prosessit0105 earth and related environmental sciencesincremental learningbusiness.industrystochastic optimizationLinear mapNonlinear systemkoneoppiminenOrdinary least squaresIncremental learning020201 artificial intelligence & image processingStochastic optimizationArtificial intelligencebusinesscomputerDistance matrices in phylogeny
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Sparse minimal learning machine using a diversity measure minimization

2019

The minimal learning machine (MLM) training procedure consists in solving a linear system with multiple measurement vectors (MMV) created between the geometric congurations of points in the input and output spaces. Such geometric congurations are built upon two matrices created using subsets of input and output points, named reference points (RPs). The present paper considers an extension of the focal underdetermined system solver (FOCUSS) for MMV linear systems problems with additive noise, named regularized MMV FOCUSS (regularized M-FOCUSS), and evaluates it in the task of selecting input reference points for regression settings. Experiments were carried out using UCI datasets, where the …

koneoppiminen
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Minimal Learning Machine: Theoretical Results and Clustering-Based Reference Point Selection

2019

The Minimal Learning Machine (MLM) is a nonlinear supervised approach based on learning a linear mapping between distance matrices computed in the input and output data spaces, where distances are calculated using a subset of points called reference points. Its simple formulation has attracted several recent works on extensions and applications. In this paper, we aim to address some open questions related to the MLM. First, we detail theoretical aspects that assure the interpolation and universal approximation capabilities of the MLM, which were previously only empirically verified. Second, we identify the task of selecting reference points as having major importance for the MLM's generaliz…

FOS: Computer and information sciencesComputer Science - Machine LearningMinimal Learning MachinekoneoppiminenStatistics - Machine Learninguniversal approximationMachine Learning (stat.ML)interpolointiapproksimointireference point selectionclusteringMachine Learning (cs.LG)
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