Real-world data is never perfect and can often suffer from corruptions (noise) that may impact interpretations of the data, models created from the data and decisions made based on...
In some key operational domains, the joint use of synthetic aperture radar (SAR) and multi-spectral sensors has shown to be a powerful tool for Earth observation. In this paper, w...
Several pattern classifiers give high classification accuracy but their storage requirements and processing time are severely expensive. On the other hand, some classifiers requir...
Alok Sharma, Kuldip K. Paliwal, Godfrey C. Onwubol...
A novel method for feature selection and construction is introduced. The method improves the classification accuracy, utilizing the well-established technique of grammatical evolu...
Dimitris Gavrilis, Ioannis G. Tsoulos, Evangelos D...
Hierarchical Incremental Class Learning (HICL) is a new task decomposition method that addresses the pattern classification problem. HICL is proven to be a good classifier but clos...
The identification of network applications through observation of associated packet traffic flows is vital to the areas of network management and surveillance. Currently popular m...
Nigel Williams, Sebastian Zander, Grenville J. Arm...
Rules are commonly used for classification because they are modular, intelligible and easy to learn. Existing work in classification rule learning assumes the goal is to produce ca...
: Applications such as Face Recognition (FR) that deal with high-dimensional data need a mapping technique that introduces representation of low-dimensional features with enhanced ...
Abstract. We consider an automated processing pipeline for tissue micro array analysis (TMA) of renal cell carcinoma. It consists of several consecutive tasks, which can be mapped ...
In this task 2,000 patents in three languages (English, French and German) were to be classified among approximately 600 categories. We used a classifier based on neural networks ...