  In this paper we outline a PhD research plan. This research contributes to
the field of one-class incremental learning and classification in case of
non-stationary environments. The goal of this PhD is to define a new
classification framework able to deal with very small learning dataset at the
beginning of the process and with abilities to adjust itself according to the
variability of the incoming data which create large scale datasets. As a
preliminary work, incremental Covariance-guided One-Class Support Vector
Machine is proposed to deal with sequentially obtained data. It is inspired
from COSVM which put more emphasis on the low variance directions while keeping
the basic formulation of incremental One-Class Support Vector Machine
untouched. The incremental procedure is introduced by controlling the possible
changes of support vectors after the addition of new data points, thanks to the
Karush-Kuhn-Tucker conditions, that have to be maintained on all previously
acquired data. Comparative experimental results with contemporary incremental
and non-incremental one-class classifiers on numerous artificial and real data
sets show that our method results in significantly better classification
performance.
