The police say it is based on the "real world recognition" that young people are going to experiment with drugs.
Hence,for real world recognition scenarios, it is important to apply the scale selection method proposed in this paper.
Consisting of 31 Likert-scale statements, CLASS-Bio probes a range of perceptions that vary between experts and novices, including enjoyment of the discipline, propensity to make connections to the real world, recognition of conceptual connections underlying knowledge, and problem-solving strategies.
Proposed method has been validated using the UCI machine learning database and further applied to a couple of real-world pattern recognition problems (the face recognition and gene expression based cancer classification).
Hence, the NW-Fisherfaces method is practical for real-world face recognition due to the low dimensionality requirement. .
In many real-world face recognition applications, this assumption may not hold as different misclassifications could lead to different losses.
On a real-world fish recognition dataset, we achieve the state-of-the-art accuracy of 98.64%.
On the other hand, most of the real-world pattern recognition tasks involve a large number of categories which may make the task difficult.
The system conducts experimental studies on two real-world activity recognition data sets and the results show the superiority of system over other ensemble algorithms.
As many real-world pattern recognition applications, the starting feature space is structured and the custom based dissimilarity measure adopted leads to a non-Euclidean dissimilarity matrix.
Many real-world pattern recognition problems can be modeled using multiple-instance learning (MIL), where instances are grouped into bags, and each bag is assigned a label.
