6533b7dcfe1ef96bd12721cc
RESEARCH PRODUCT
OnMLM: An Online Formulation for the Minimal Learning Machine
Tommi KärkkäinenCésar Lincoln C. MattosAjalmar R. Da Rocha NetoJoão P. P. GomesAlan L. S. Matiassubject
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 phylogenydescription
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 experiments, in both classification and regression scenarios, indicate its feasibility for applications that require an online learning framework. peerReviewed
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-01-01 |