In our previous study, we visualized microarray data of hepatocellular carcinoma (HCC) by using self-organizing-map, and investigated molecular signature representing the development of HCC. In this study, we propose two visualization methods of microarray data with Euclidean distance classifiers and Sammonpsilas nonlinear mapping. Our proposed methods will serve as tool to discover molecular signature representing the development of HCC for molecular biologists or doctors.

A bootstrap technique for nearest neighbor classifier design is proposed. Our primary interest in designing a classifier is in small training sample size situations. Conventional bootstrapping techniques sample the training samples with replacement. On the other hand, our technique generates bootstrap samples by locally combining original training samples. The nearest neighbor classifier is designed on the bootstrap samples and is tested on the test samples independent of training samples. The performance of the proposed classifier is demonstrated on three artificial data sets and one real data set. Experimental results show that the nearest neighbor classifier designed on the bootstrap samples outperforms the conventional k-NN classifiers as well as the edited 1-NN classifiers, particularly in high dimensions.

We study a Gabor filter-based feature extraction method for handwritten numeral character recognition. The performance of the Gabor filter-based method is demonstrated on the ETL-1 database. Experimental results suggest that the Gabor filter-based method should be considered in recognition of handwritten numeric characters.

It is widely believed in the pattern recognition field that when a fixed number of training samples is used to design a classifier, the generalization error of the classifier tends to increase as the number of features gets larger. In this paper, we discuss the generalization error of the artificial neural network (ANN) classifiers in high-dimensional spaces, under a practical condition that the ratio of the training sample size to the dimensionality is small. Experimental results show that the generalization error of ANN classifiers seems much less sensitive to the feature size than 1-NN, Parzen and quadratic classifiers.

A method for handprinted Chinese character recognition based on Gabor filters is proposed. The Gabor approach to character recognition is intuitively appealing because it is inspired by a multi-channel filtering theory for processing visual information in the early stages of the human visual system. The performance of a character recognition system using Gabor features is demonstrated on the ETL-8 character set. Mental results show that the Gabor features yielded an error rate of 2.4% versus the error rate of 4.4% obtained by using a popular feature extraction method.