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In this paper, we argue that for a C-class classification problem, C 2-class classifiers, each of which discriminating one class from the other classes and having a characteristic input feature subset, should in general outperform, or at least match the performance of, a C-class classifier with one single input feature subset. For each class, we select a(More)
Since the single nucleotide polymorphisms (SNPs) are genetic variations which determine the difference between any two unrelated individuals, the SNPs can be used to identify the correct source population of an individual. For efficient population identification with the HapMap genotype data, as few informative SNPs as possible are required from the(More)
Single nucleotide polymorphisms (SNPs) are genetic variations that determine the differences between any two unrelated individuals. Various population groups can be distinguished from each other using SNPs. For instance, the HapMap dataset has four population groups with about ten million SNPs. For more insights on human evolution, ethnic variation, and(More)
The single nucleotide polymorphisms (SNPs) are believed to determine human differences and, to some degree, provide biomedical researchers a possibility of predicting risks of some diseases and explaining patients’ different responses to drug regimens. With the availability of millions of SNPs in the Hapmap Project, although large amount of information(More)
This chapter introduces an approach to class-dependent feature selection and a novel support vector machine (SVM). The relative background and theory are presented for describing the proposed method, and real applications of the method on several biomedical datasets are demonstrated in the end. The authors hope this chapter can provide readers a different(More)
In this paper, we attempt to answer the following question with systematic computer simulations: for the same validation error rate, does the size of a feedforward neural network matter? This is related to the so-called Occam's Razor, that is, with all things being equal, the simplest solution is likely to work the best. Our simulation results indicate that(More)
Feature extraction and feature selection are very important steps for face recognition. In this paper, we propose to use a classdependent feature selection method to select different feature subsets for different classes after using principal component analysis to extract important information from face images. We then use the support vector machine (SVM)(More)
Microsatellite primer PCR (SSRP-PCR) technique was used to investigate the genetic diversity in Oedaleus Fieber and provide insights into the geographical origin of Taiyuan population. The proportion of polymorphic loci by SSRP markers indicated that three populations in Oedaleus asiaticus B.-Bienko had remarkable genetic variation (85.7–96.9%)(More)