An Introduction to Support Vector Machines and Other Kernel-based Learning MethodsThis 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. |
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Tulokset 1 - 5 kokonaismäärästä 16
Sivu vi
... 7.5.1 Analytical Solution for Two Points 138 7.5.2 Selection Heuristics 140 7.6 Techniques for Gaussian Processes 144 7.7 Exercises 145 7.8 Further Reading and Advanced Topics 146 vi Contents.
... 7.5.1 Analytical Solution for Two Points 138 7.5.2 Selection Heuristics 140 7.6 Techniques for Gaussian Processes 144 7.7 Exercises 145 7.8 Further Reading and Advanced Topics 146 vi Contents.
Sivu 5
They can be used to motivate heuristic trade-offs between complexity and accuracy and various principles have been ... avoid settling for heuristics that work well on small examples, but break down once larger training sets are used.
They can be used to motivate heuristic trade-offs between complexity and accuracy and various principles have been ... avoid settling for heuristics that work well on small examples, but break down once larger training sets are used.
Sivu 6
The fourth problem is that frequently the learning algorthm is controlled by a large number of parameters that are often chosen by tuning heuristics, making the system difficult and unreliable to use. Despite the drawbacks, there have ...
The fourth problem is that frequently the learning algorthm is controlled by a large number of parameters that are often chosen by tuning heuristics, making the system difficult and unreliable to use. Despite the drawbacks, there have ...
Sivu 17
A number of heuristics have been proposed for this problem, for example the pocket algorithm outputs the w that survived for the longest number of iterations. The extension suggested in the previous remark could be used to derive a ...
A number of heuristics have been proposed for this problem, for example the pocket algorithm outputs the w that survived for the longest number of iterations. The extension suggested in the previous remark could be used to derive a ...
Sivu 25
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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 |
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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
Kirjaluettelon tiedot
| Otsikko | An Introduction to Support Vector Machines and Other Kernel-based Learning Methods |
| Kirjailijat | Nello Cristianini, John Shawe-Taylor |
| Kustantaja | Cambridge University Press, 2000 |
| ISBN | 1139643630, 9781139643634 |
| Vie sitaatti | BiBTeX EndNote RefMan |