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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
ISBN: 0521780195, 9780521780193
Publisher: Cambridge University Press
Format: chm

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, . It just struck me as an odd coincidence. A Support Vector Machine provides a binary classification mechanism based on finding a hyperplane between a set of samples with +ve and -ve outputs. My experience in machine learning indicates that seemingly different algorithmic/mathematical methods can be combined into a unified and coherent framework. It includes two phases: Training phase: Learn a model from training data; Predicting phase: Use the model to predict the unknown or future outcome . "An Introduction to Support Vector Machines and Other Kernel-based Learning Methods". Nello Cristianini, John Shawer-Taylor [2] 数据挖掘中的新方法-支持向量机 邓乃扬， 田英杰 [3] 机器学习. [1] An Introduction to Support Vector Machines and other kernel-based learning methods. Predictive Analytics is about predicting future outcome based on analyzing data collected previously. "Boosting" is another approach in Ensemble Method.