TY  - EDBOOK
AB  - Incremental on-line learning is a research topic gaining increasing interest in the machine learning community. Such learning methods are highly adaptive, not restricted to distinct training and application phases, and applicable to large volumes of data. In this paper, we present a novel classifier based on the unsupervised topology-learning TopoART neural network. We demonstrate that this classifier is capable of fast incremental on-line learning and achieves excellent results on standard datasets. We further show that it can successfully process imbalanced, incomplete, and noisy data. Due to these properties, we consider it a promising component for constructing artificial agents operating in real-world environments.
DA  - 2012
KW  - TopoART
KW  - on-line learning
KW  - incremental learning
KW  - Adaptive Resonance Theory
KW  - classification
LA  - eng
PY  - 2012
TI  - An Incremental On-line Classifier for Imbalanced, Incomplete, and Noisy Data
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-25191229
Y2  - 2024-11-22T04:55:37
ER  -