An Introduction to Support Vector Machines and Other Kernel-based Learning MethodsCambridge University Press, 23.3.2000 This is the first comprehensive introduction to Support Vector Machines (SVMs), a generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software. |
Kirjan sisältä
Tulokset 1 - 5 kokonaismäärästä 61
Sivu ix
... chapters, as well as pointers to relevant literature and on-line software and articles. Given the potential instability of on-line material, in some cases the book points to a dedicated website, where the relevant links will be kept ...
... chapters, as well as pointers to relevant literature and on-line software and articles. Given the potential instability of on-line material, in some cases the book points to a dedicated website, where the relevant links will be kept ...
Sivu x
... chapter finishes with a section entitled Further Reading and Advanced Topics, which fulfils two functions. First by moving all the references into this section we have kept the main text as uncluttered as possible. Again we ask for the ...
... chapter finishes with a section entitled Further Reading and Advanced Topics, which fulfils two functions. First by moving all the references into this section we have kept the main text as uncluttered as possible. Again we ask for the ...
Sivu xi
Nello Cristianini, John Shawe-Taylor. helped to create the pictures for Chapter 6 which were generated using his software package at the University of Bristol. We would like to thank John Platt for providing the SMO pseudocode included ...
Nello Cristianini, John Shawe-Taylor. helped to create the pictures for Chapter 6 which were generated using his software package at the University of Bristol. We would like to thank John Platt for providing the SMO pseudocode included ...
Sivu 1
... chapter we will introduce the important components of the learning methodology, give an overview of the different kinds of learning and discuss why this approach has such a strategic importance. After the framework of the learning ...
... chapter we will introduce the important components of the learning methodology, give an overview of the different kinds of learning and discuss why this approach has such a strategic importance. After the framework of the learning ...
Sivu 4
... Chapter 4, when we will motivate the particular models we shall be using. 1.3. Improving. Generalisation. The generalisation criterion places an altogether different constraint on the learning algorithm. This is most amply illustrated by ...
... Chapter 4, when we will motivate the particular models we shall be using. 1.3. Improving. Generalisation. The generalisation criterion places an altogether different constraint on the learning algorithm. This is most amply illustrated by ...
Sisältö
1 | |
9 | |
KernelInduced Feature Spaces | 26 |
Generalisation Theory | 52 |
Optimisation Theory | 79 |
Support Vector Machines | 93 |
Implementation Techniques | 125 |
Applications of Support Vector Machines | 149 |
A Pseudocode for the SMO Algorithm | 162 |
References | 173 |
Index | 187 |
Muita painoksia - Näytä kaikki
An Introduction to Support Vector Machines and Other Kernel-based Learning ... Nello Cristianini,John Shawe-Taylor Rajoitettu esikatselu - 2000 |
An Introduction to Support Vector Machines and Other Kernel-based Learning ... Nello Cristianini,John Shawe-Taylor Esikatselu ei käytettävissä - 2000 |
Yleiset termit ja lausekkeet
1-norm soft margin algorithm analysis applied approach Bayesian bias bound Chapter choice classification computational consider constraints convergence convex corresponding datasets Definition described dual problem dual representation fat-shattering dimension feasibility gap feature mapping feature space finite Gaussian processes generalisation error geometric margin given Hence heuristics high dimensional Hilbert space hyperplane hypothesis inequality inner product space input space introduced iterative Karush-Kuhn-Tucker kernel function kernel matrix Lagrange multipliers Lagrangian learning algorithm linear functions linear learning machines loss function machine learning margin distribution margin slack vector maximal margin hyperplane maximise minimise norm objective function obtained on-line optimisation problem parameters perceptron perceptron algorithm performance positive semi-definite primal and dual quantity random examples real-valued function Remark result ridge regression Section sequence slack variables soft margin optimisation solution solve subset Support Vector Machines SVMs techniques Theorem training data training examples training points training set update Vapnik VC dimension weight vector zero