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 6 - 10 kokonaismäärästä 37
Sivu 9
... analysis of their generalisation properties, provide the framework within which the construction of more complex systems will be developed in the coming chapters. In this chapter we review results from the literature that will be ...
... analysis of their generalisation properties, provide the framework within which the construction of more complex systems will be developed in the coming chapters. In this chapter we review results from the literature that will be ...
Sivu 14
Nello Cristianini, John Shawe-Taylor. Proof For the analysis we augment the input vectors by an extra coordinate with value R. We denote the new vector by x, = (x|, R)', where x' denotes the transpose of x. Similarly we add an extra ...
Nello Cristianini, John Shawe-Taylor. Proof For the analysis we augment the input vectors by an extra coordinate with value R. We denote the new vector by x, = (x|, R)', where x' denotes the transpose of x. Similarly we add an extra ...
Sivu 18
... larger a, can be used to rank the data according to their information content. Indeed, in the analysis of the simple perceptron algorithm we have already found many of the important concepts. 18 2 Linear Learning Machines.
... larger a, can be used to rank the data according to their information content. Indeed, in the analysis of the simple perceptron algorithm we have already found many of the important concepts. 18 2 Linear Learning Machines.
Sivu 29
Katseluoikeutesi tähän teokseen on päättynyt.
Katseluoikeutesi tähän teokseen on päättynyt.
Sivu 32
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Katseluoikeutesi tähän teokseen on päättynyt.
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