جمعه 19 مرداد 1397
نویسنده: George Broderick
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods John Shawe-Taylor, Nello Cristianini
Publisher: Cambridge University Press
John; An Introduction to Support Vector Machines and other kernel-based. In this work, we provide extended details of our methodology and also present analysis that tests the performance of different supervised machine learning methods and investigates the discriminative influence of the proposed features. Princeton, NJ: Princeton University Press. 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. Machine-learning approaches, which include neural networks, hidden Markov models, belief networks, support vector and other kernel-based machines, are ideally suited for domains characterized by the existence of large amounts of data, . An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Such as statistical learning theory and Support Vector Machines,. Mathematical methods in statistics. Cristianini, N., & Shawe-Taylor, J. Specifically, we trained individual support vector machine (SVM) models  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. Introduction to support vector machines and other kernel-based learning methods. Nello Cristianini, John Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods 2000 | pages: 189 | ISBN: 0521780195. Scale models using state-of-the-art machine learning methods for. Shawe-Taylor, An Introduction to Support Vector Machines: And Other Kernel-Based Learning Methods, Cambridge University Press, New York, NY, 2000.