Sparse kernel methods for high-dimensional survival data
نویسندگان
چکیده
منابع مشابه
Sparse kernel methods for high-dimensional survival data
UNLABELLED Sparse kernel methods like support vector machines (SVM) have been applied with great success to classification and (standard) regression settings. Existing support vector classification and regression techniques however are not suitable for partly censored survival data, which are typically analysed using Cox's proportional hazards model. As the partial likelihood of the proportiona...
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ژورنال
عنوان ژورنال: Bioinformatics
سال: 2008
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/btn253