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 51
Sivu vi
... Kernel Ridge Regression 118 6.2.3 Gaussian Processes 120 6.3 Discussion 121 6.4 Exercises 121 6.5 Further Reading and Advanced Topics 122 7 Implementation Techniques 125 7.1 General Issues 125 7.2 The Naive Solution: Gradient Ascent ...
... Kernel Ridge Regression 118 6.2.3 Gaussian Processes 120 6.3 Discussion 121 6.4 Exercises 121 6.5 Further Reading and Advanced Topics 122 7 Implementation Techniques 125 7.1 General Issues 125 7.2 The Naive Solution: Gradient Ascent ...
Sivu 1
... the key to its solution. In this 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.
... the key to its solution. In this 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.
Sivu 2
The estimate of the target function which is learnt or output by the learning algorithm is known as the solution of the learning problem. In the case of classification this function is sometimes referred to as the decision function.
The estimate of the target function which is learnt or output by the learning algorithm is known as the solution of the learning problem. In the case of classification this function is sometimes referred to as the decision function.
Sivu 5
For the first class there exist algorithms that run in time polynomial in the size of the input, while for the second the existence of such an algorithm would imply that any problem for which we can check a solution in polynomial time ...
For the first class there exist algorithms that run in time polynomial in the size of the input, while for the second the existence of such an algorithm would imply that any problem for which we can check a solution in polynomial time ...
Sivu 6
Secondly, it appears that we can also avoid much of the laborious design and programming inherent in the traditional solution methodology, at the expense of collecting some labelled data and running an off-the-shelf algorithm for ...
Secondly, it appears that we can also avoid much of the laborious design and programming inherent in the traditional solution methodology, at the expense of collecting some labelled data and running an off-the-shelf algorithm for ...
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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 |