When asked to list semantic features for concrete concepts, participants list many features for some concepts and few for others. Concepts with many semantic features have been reported to be processed faster in lexical decision, naming, and semantic decision tasks (Pexman, Holyk, & Monfils, 2003; Pexman, Lupker, & Hino, 2002). Using a much larger and better controlled set of items in Experimen...

Recent theories suggest that both risk and mispricing are associated with commonality in security returns, and that the loadings on characteristic-based factors can be used to predict future returns. We offer a parsimonious model which features: (1) a factor motivated by limited attention that is dominant in explaining short-horizon anomalies, and (2) a factor motivated by overconfidence that i...

A novel copy-move forgery detection scheme using adaptive over-segmentation and SURF is proposed in this paper. The proposed scheme integrates both block-based and keypoint-based forgery detection methods. First, the proposed Adaptive Over-Segmentation algorithm segments the host image into non-overlapping and irregular blocks adaptively. Then, the feature points are extracted from each block a...

Feature selection method for text classification based on information gain ranking, improved by removing redundant terms using mutual information measure and inclusion index, is proposed. We report an experiment to study the impact of term redundancy on the performance of text classifier. The result shows that term redundancy behaves very similar to noise and may degrade the classifier performa...

Feature selection can significantly be decisive when analyzing high dimensional data, especially with a small number of samples. Feature extraction methods do not have decent performance in these conditions. With small sample sets and high dimensional data, exploring a large search space and learning from insufficient samples becomes extremely hard. As a result, neural networks and clustering a...