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 31
Sivu vii
7.7 Exercises 145 7.8 Further Reading and Advanced Topics 146 8 Applications of Support Vector Machines 149 8.1 Text Categorisation 150 8.1.1 A Kernel from IR Applied to Information Filtering .... 150 8.2 Image Recognition 152 8.2.1 ...
7.7 Exercises 145 7.8 Further Reading and Advanced Topics 146 8 Applications of Support Vector Machines 149 8.1 Text Categorisation 150 8.1.1 A Kernel from IR Applied to Information Filtering .... 150 8.2 Image Recognition 152 8.2.1 ...
Sivu 1
We will refer to this approach to problem solving as the learning methodology The same reasoning applies to the problem ... When computers are applied to solve a practical problem it is usually the case that the method of deriving the ...
We will refer to this approach to problem solving as the learning methodology The same reasoning applies to the problem ... When computers are applied to solve a practical problem it is usually the case that the method of deriving the ...
Sivu 2
As computers are applied to solve more complex problems, however, situations can arise in which there is no known method for computing the desired output from a set of inputs, or where that computation may be very expensive.
As computers are applied to solve more complex problems, however, situations can arise in which there is no known method for computing the desired output from a set of inputs, or where that computation may be very expensive.
Sivu 3
... of this book is a family of techniques for learning to perform input/output mappings from labelled examples for the most part in the batch setting, that is for applying the supervised learning methodology from batch training data.
... of this book is a family of techniques for learning to perform input/output mappings from labelled examples for the most part in the batch setting, that is for applying the supervised learning methodology from batch training data.
Sivu 22
... the pseudo-inverse can be used, or else the technique of ridge regression described below can be applied. In the 1960s attention was paid to the construction of simple iterative procedures for training linear learning machines.
... the pseudo-inverse can be used, or else the technique of ridge regression described below can be applied. In the 1960s attention was paid to the construction of simple iterative procedures for training linear learning machines.
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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 |