TY - JOUR
TI - On Certain Limitations of Recursive Representation Model
AU - Jastrzębski, Stanisław
AU - Sieradzki, Igor
TI - On Certain Limitations of Recursive Representation Model
AB - 
	There is a strong research eort towards developing models that can achieve state-of-the-art results without sacrificing interpretability and simplicity. One of such is recently proposed Recursive Random Support Vector Machine (R2SVM) model, which is composed of stacked linear models. R2SVM was reported to learn deep representations outperforming many strong classiffiers like Deep Convolutional Neural Network. In this paper we try to analyze it both from theoretical and empirical perspective and show its important limitations.Analysis of similar model Deep Representation Extreme Learning Machine (DrELM) is also included. It is concluded that models in its current form achieves lower accuracy scores than Support Vector Machine with Radial Basis Function kernel.
VL - 2016
IS - Volume 25
PY - 2017
SN - 1732-3916
C1 - 2083-8476
SP - 37
EP - 47
DO - 10.4467/20838476SI.16.003.6184
UR - https://ejournals.eu/en/journal/schedae-informaticae/article/on-certain-limitations-of-recursive-representation-model
KW - support vector machines
KW - random recursive support vector machine
KW - extreme learning machine
KW - representation learning
KW - stacked generalization