10 citations · 12 across the 7 of their papers we have counts for
7 papers
Mislabel Detection of Finnish Publication Ranks
Anton Akusok, Mirka Saarela, Tommi Kärkkäinen +2
The paper proposes to analyze a data set of Finnish ranks of academic publication channels with Extreme Learning Machine (ELM). The purpose is to introduce and test recently propos…
Per-sample Prediction Intervals for Extreme Learning Machines
Anton Akusok, Yoan Miche, Kaj-Mikael Björk +1
Prediction intervals in supervised Machine Learning bound the region where the true outputs of new samples may fall. They are necessary in the task of separating reliable predictio…
Extreme Learning Tree
Anton Akusok, Emil Eirola, Kaj-Mikael Björk +1
The paper proposes a new variant of a decision tree, called an Extreme Learning Tree. It consists of an extremely random tree with non-linear data transformation, and a linear obse…
Spiking Networks for Improved Cognitive Abilities of Edge Computing Devices
Anton Akusok, Kaj-Mikael Björk, Leonardo Espinosa Leal +3
This concept paper highlights a recently opened opportunity for large scale analytical algorithms to be trained directly on edge devices. Such approach is a response to the arising…
A Web Page Classifier Library Based on Random Image Content Analysis Using Deep Learning
Leonardo Espinosa Leal, Kaj-Mikael Björk, Amaury Lendasse +1
In this paper, we present a methodology and the corresponding Python library 1 for the classification of webpages. Our method retrieves a fixed number of images from a given webpag…
Incremental ELMVIS for unsupervised learning
Anton Akusok, Emil Eirola, Yoan Miche +5
An incremental version of the ELMVIS+ method is proposed in this paper. It iteratively selects a few best fitting data samples from a large pool, and adds them to the model. The me…