S. Y. Kung

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Dimensionality reduction plays an important role in machine learning techniques. In classification, data transformation aims to reduce the number of feature dimensions, whereas attempts to enhance the class separability. To this end, we propose a new classifier-independent criterion called " Sum-of-Signal-to-Noise-Ratio " (SoSNR). A framework designed for(More)
Kernel techniques for classification is especially challenging in terms of computation and memory requirement when data fall into more than two categories. In this paper, we extend a binary classification technique called Ridge-adjusted Slack Variable Optimization (RiSVO) to its multiclass counterpart where the label information encoding scheme allows the(More)
The problem of identifying genetic factors under­ lying complex and quantitative traits such as height, weight and disease susceptibility in natural popula­ tions has become a major theme of research in recent years. Aiming at revealing the inter-dependency and causal relationship between the underlying genotypes and observed phenotypes, researchers from(More)
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