title: SIM-ELM: Connecting the ELM model with similarity-function learning.
abstract: This paper moves from the affinities between two well-known learning schemes that apply randomization in the training process, namely, Extreme Learning Machines (ELMs) and the learning framework using similarity functions. These paradigms share a common approach involving data remapping and linear separators, but differ in the role of randomization within the respective learning algorithms. The paper presents an integrated approach connecting the two models, which ultimately yields a new variant of the basic ELM. The resulting learning scheme is characterized by an analytical relationship between the dimensionality of the remapped space and the learning abilities of the eventual predictor. Experimental results confirm that the new learning scheme can improve over conventional ELM in terms of the trade-off between classification accuracy and predictor complexity (i.e., the dimensionality of the remapped space).
authors.affiliation: Department of Electrical, Electronics, and Telecommunications Engineering and Naval Architecture (DITEN), University of Genoa, Genova, Italy. Electronic address: paolo.gastaldo@unige.it.
authors.forename: Paolo
authors.initials: P
authors.lastname: Gastaldo
authors.affiliation: Department of Electrical, Electronics, and Telecommunications Engineering and Naval Architecture (DITEN), University of Genoa, Genova, Italy. Electronic address: federica.bisio@edu.unige.it.
authors.forename: Federica
authors.initials: F
authors.lastname: Bisio
authors.affiliation: Istituto Italiano di Tecnologia, IIT, Morego, Genova, Italy. Electronic address: sergio.decherchi@iit.it.
authors.forename: Sergio
authors.initials: S
authors.lastname: Decherchi
authors.affiliation: Department of Electrical, Electronics, and Telecommunications Engineering and Naval Architecture (DITEN), University of Genoa, Genova, Italy. Electronic address: rodolfo.zunino@unige.it.
authors.forename: Rodolfo
authors.initials: R
authors.lastname: Zunino
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D006331:Heart Diseases / Q000453:epidemiology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016014:Linear Models; D000069550:Machine Learning; D016571:Neural Networks, Computer; D011897:Random Allocation; D015203:Reproducibility of Results
keywords: Extreme learning machine; Randomization in learning; Similarity functions; Supervised learning