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An Introduction to Support Vector Machines and Other Kernel-based Learning Methods by John Shawe-Taylor, Nello Cristianini

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods John Shawe-Taylor, Nello Cristianini ebook
Page: 189
Format: chm
ISBN: 0521780195, 9780521780193
Publisher: Cambridge University Press

Specifically, we trained individual support vector machine (SVM) models [26] for 203 yeast TFs using 2 types of features: the existence of PSSMs upstream of genes and chromatin modifications adjacent to the ATG start codons. Support vector machines map input vectors to a higher dimensional space where a maximal separating hyperplane is constructed. CRISTIANINI, N.; SHAWE-TAYLOR, J. Support Vector Machine (SVM) is a supervised learning algorithm developed by Vladimir Vapnik and his co-workers at AT&T Bell Labs in the mid 90's. "Boosting" is another approach in Ensemble Method. A Support Vector Machine provides a binary classification mechanism based on finding a hyperplane between a set of samples with +ve and -ve outputs. Predictive Analytics is about predicting future outcome based on analyzing data collected previously. Bounds the influence of any single point on the decision boundary, for derivation, see Proposition 6.12 in Cristianini/Shaw-Taylor's "An Introduction to Support Vector Machines and Other Kernel-based Learning Methods". An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge: Cambridge University Press, 2000. The models were trained and tested using TF target genes from Cristianini N, Shawe-Taylor J: An Introduction to Support Vector Machines and other kernel-based learning methods. It includes two phases: Training phase: Learn a model from training data; Predicting phase: Use the model to predict the unknown or future outcome .