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. |
Kirjan sisältä
Tulokset 1 - 5 kokonaismäärästä 39
Sivu vi
... 69 4.5 Generalisation for Regression 70 4.6 Bayesian Analysis of Learning 74 4.7 Exercises 76 4.8 Further Reading and Advanced Topics 76 5 Optimisation Theory 79 5.1 Problem Formulation 79 5.2 Lagrangian Theory 81 5.3 Duality 87 5.4 ...
... 69 4.5 Generalisation for Regression 70 4.6 Bayesian Analysis of Learning 74 4.7 Exercises 76 4.8 Further Reading and Advanced Topics 76 5 Optimisation Theory 79 5.1 Problem Formulation 79 5.2 Lagrangian Theory 81 5.3 Duality 87 5.4 ...
Sivu ix
... (SVMs) as well as algorithmic strategies for implementing them, and applications of the approach to practical problems. ... including dual representations, feature spaces, learning theory, optimisation theory, and algorithmics.
... (SVMs) as well as algorithmic strategies for implementing them, and applications of the approach to practical problems. ... including dual representations, feature spaces, learning theory, optimisation theory, and algorithmics.
Sivu 9
We will first discuss algorithms and issues of classification, and then we will move on to the problem of regression. ... cases such machines can be represented in a particularly useful form, which we will call the dual representation.
We will first discuss algorithms and issues of classification, and then we will move on to the problem of regression. ... cases such machines can be represented in a particularly useful form, which we will call the dual representation.
Sivu 19
We can therefore view the 1-norm of a as a measure of the complexity of the target concept in the dual representation. ... 2.1.2 Other Linear Classifiers The problem of learning a hyperplane that separates two (separable) sets of points ...
We can therefore view the 1-norm of a as a measure of the complexity of the target concept in the dual representation. ... 2.1.2 Other Linear Classifiers The problem of learning a hyperplane that separates two (separable) sets of points ...
Sivu 24
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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 |