Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning

Active machine learning algorithms are used when large numbers of unlabeled examples are available and getting labels for them is costly (e.g. requiring consulting a human expert). Many conventional active learning algorithms focus on refining the decision boundary, at the expense of exploring new regions that the current hypothesis misclassifies. We propose a new active learning algorithm that balances such exploration with refining of the decision boundary by dynamically adjusting the probability to explore at each step. Our experimental results demonstrate improved performance on data sets that require extensive exploration while remaining competitive on data sets that do not. Our algorithm also shows significant tolerance of noise.

The Chromosomal Complement of the Yellow-Cheeked Vole

The karyotype of Microtus xanthognathus (Leach) is described, based on material from one female and one male vole. The diploid chromosomal number was found to be 54, and the fundamental number 62. The metacentric X-chromosome was of medium size and averaged 6.6% of the haploid complement. The designated Y-chromosome was near acrocentric. The specific distinction of M. xanthognathus and Microtus chrotorrhinus (Miller) was confirmed by the recognition of major differences in karyotype and differences in fundamental number. The distributional history of M. xanthognathus is briefly discussed.