To train good supervised and semi-supervised object classifiers, it is critical that we not waste the time of the human experts who are providing the training labels. Existing active learning strategies can have uneven performance, being efficient on some datasets but wasteful on others, or inconsistent just between runs on the same dataset. We propose perplexity based graph construction and a new hierarchical subquery evaluation algorithm to combat this variability, and to release the potential of Expected Error Reduction. Under some specific circumstances, Expected Error Reduction has been one of the strongest-performing informativeness criteria for active learning. Until now, it has also been prohibitively costly to compute for sizeable datasets. We demonstrate our highly practical algorithm, comparing it to other active learning measures on classification datasets that vary in sparsity, dimensionality, and size. Our algorithm is consistent over multiple runs and achieves high accuracy, while querying the human expert for labels at a frequency that matches their desired time budget.
The invention is an improved apparatus for the removal of sodium carbonate from cyanide plating baths. The method involves the precipitation of sodium carbonate by the effect of cooling the cyanide plating bath liquid. A container is submerged in the solution with the container opening extending above the plating bath solution level. The container is filled with dry ice and water which produces a temperature of approximately zero degrees centigrade inside of the container. The solution adjacent to the exterior of the container is cooled so that excess sodium carbonate is precipitated as a crystalline deposit. After a desired interval, the container is removed with the encrustation of sodium carbonate for disposal.