3 papers
cs.LG2019
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…
cs.LG2019
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…
cs.LG2019
Comparison of Classification Methods for Very High-Dimensional Data in Sparse Random Projection Representation
Anton Akusok, Emil Eirola
The big data trend has inspired feature-driven learning tasks, which cannot be handled by conventional machine learning models. Unstructured data produces very large binary matrice…