CompareSVM: supervised, Support Vector Machine (SVM) inference of gene regularity networks
نویسندگان
چکیده
منابع مشابه
Tutorial on Support Vector Machine (SVM)
In this tutorial we present a brief introduction to SVM, and we discuss about SVM from published papers, workshop materials & material collected from books and material available online on the World Wide Web. In the beginning we try to define SVM and try to talk as why SVM, with a brief overview of statistical learning theory. The mathematical formulation of SVM is presented, and theory for the...
متن کاملBudgeted Semi-supervised Support Vector Machine
Due to the prevalence of unlabeled data, semisupervised learning has drawn significant attention and has been found applicable in many realworld applications. In this paper, we present the so-called Budgeted Semi-supervised Support Vector Machine (BS3VM), a method that leverages the excellent generalization capacity of kernel-based method with the adjacent and distributive information carried i...
متن کاملLocality Preserving Semi-Supervised Support Vector Machine
Manifold regularization, which learns from a limited number of labeled samples and a large number of unlabeled samples, is a powerful semi-supervised classifier with a solid theoretical foundation. However, manifold regularization has the tendency to misclassify data near the boundaries of different classes during the classification process. In this paper, we propose a novel classification meth...
متن کاملCost-Sensitive Semi-Supervised Support Vector Machine
In this paper, we study cost-sensitive semi-supervised learning where many of the training examples are unlabeled and different misclassification errors are associated with unequal costs. This scenario occurs in many real-world applications. For example, in some disease diagnosis, the cost of erroneously diagnosing a patient as healthy is much higher than that of diagnosing a healthy person as ...
متن کاملTV-SVM: Total Variation Support Vector Machine for Semi-Supervised Data Classification
We introduce semi-supervised data classification algorithms based on total variation (TV), Reproducing Kernel Hilbert Space (RKHS), support vector machine (SVM), Cheeger cut, labeled and unlabeled data points. We design binary and multi-class semi-supervised classification algorithms. We compare the TV-based classification algorithms with the related Laplacian-based algorithms, and show that TV...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: BMC Bioinformatics
سال: 2014
ISSN: 1471-2105
DOI: 10.1186/s12859-014-0395-x