Issue No. 08 - August (1998 vol. 20)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.709619
<p><b>Abstract</b>—The classical theory of pattern recognition assumes labeled examples appear according to unknown underlying class conditional probability distributions where the pattern classes are picked randomly in a passive manner according to their a priori probabilities. This paper presents experimental results for an incremental nearest-neighbor learning algorithm which actively selects samples from different pattern classes according to a querying rule as opposed to the a priori probabilities. The amount of improvement of this query-based approach over the passive batch approach depends on the complexity of the Bayes rule.</p>
Incremental learning, sample querying, nearest-neighbor algorithm, active learning, model selection.
J. Ratsaby, "Incremental Learning With Sample Queries," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 20, no. , pp. 883-888, 1998.