Crowdsourcing has been recognized as a possible technique to complement costly user studies, usability studies, relevance judgment for information retrieval studies, and training set build-up for automatic document classification. However, the quality of crowdworkers varies by diverse factors and we often cannot tell whether their answers are right or wrong immediately due to the lack of gold standard answers. In this paper, we present a machine-learning based crowdworker filtering technique that can be used to assess workers immediately after they finish their assigned tasks. A Support Vector Machine (SVM)-based crowdworker filter, called a Smart Crowd Filter (SCFilter), was used to predict the probability that each label is correct and identifies those crowdworkers that consistently provide answers that are unlikely to be correct. To verify the performance of the SCFilter, a bad worker detection simulation test and an experiment in an actual crowdsourcing environment at the Amazon Mechanical Turk (AMT) website were performed. In the simulation test, bad worker detection performance was assessed in terms of precision and recall. In the experiment at the AMT website, a statistically significant improvement was observed for automatic document classification.