The algorithm combines active learning, biased classification and incremental learning to model the small sample biased learning problem in relevance feedback process. 该算法将主动式学习、有偏分类和增量学习结合起来,对相关反馈过程中的小样本有偏学习问题进行建模。
An inadequate sample size, a biased sample, a non-unique concept, and scientific flaws in the study are common faults. 样本大小不足,有偏差的样本,非独特的观念和研究中的科学缺陷。
Is it possible for an estimator to be biased in finite sample but consistent in large sample? 一个估计量是否有可能在有限样本中是有偏的但又具有一致性?