.................................................................................................................................. iii ACKNOWLEDGMENTS ............................................................................................................. iv LIST OF TABLES .........................................................................................................................

Data mining techniques can be used to discover useful patterns by exploring and analyzing data, so, it is feasible to incorporate data mining techniques into the classification process to discover useful patterns or classification rules from training samples. This paper thus proposes a data mining technique to discover fuzzy classification rules based on the well-known Apriori algorithm. Signif...

Classification accuracy of single exams is well studied in the educational measurement literature. However, when making important decisions, such as certification decisions, one usually uses several exams: an exam set. This chapter elaborates on classification accuracy of exam sets. This is influenced by the shape of the ability distribution, the height of the standards, and the possibility for...

Abstract: The randomness and fuzziness of digital image semantics are hot issues in the field of classification technology, so a new semantic representation of digital image based on cloud model is proposed and a semantic vector space is constructed. In the space, semantic classifications of digital images are completed by calculating the semantic class certainty degree (SCCD). In addition, we ...

Due to the expected increase of defects in circuits based on deep submicron technologies, reliability has become an important design criterion. Although different approaches have been developed to estimate reliability in digital circuits and some measuring concepts have been separately presented to reveal the quality of analog circuit reliability in the literature, there is a gap to estimate re...

In this paper, a stomach cancer detection system based on Artificial Neural Network (ANN), and the Discrete Cosine Transform (DCT), is developed. The proposed system extracts classification features from stomach microarrays using the DCT. The features extracted from the DCT coefficients are then applied to an ANN for classification (tumor or non—tumor). The microarray images used in this study ...

The AFSSI-C is a spectral imager that generates spectral classification directly, in fewer measurements than are required by traditional systems that measure the spectral datacube (which is later interpreted to make material classification). By utilizing adaptive features to constantly update conditional probabilities for the different hypotheses, the AFSSI-C avoids the overhead of directly mea...

Genetic algorithm utilizing image clustering with merge and split processes which allows minimizing Fisher distance between clusters is proposed. Through experiments with simulation and real remote sensing satellite imagery data, it is found that the proposed clustering method is superior to the conventional k-means and ISODATA clustering methods in comparison to the geographic maps and classif...

I review the differences between classical and quantum systems, emphasizing the connection between no-hidden variable theorems and superior computational power of quantum computers. Using quantum lattice gas automata as examples, I describe possibilities for efficient simulation of quantum and classical systems with a quantum computer. I conclude with a list of research directions. 2001 Physics...

This paper proposes unconstrained functional networks classifier as a novel approach for solving pattern classification problems. Both initial topology and learning methodology of the new unconstrained functional networks classifier are presented. The performance of the new networks classifier is examined using both real-data and simulation studies. A comparative study with the most common clas...