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 79
Sivu vii
... 157 8.4.2 Gene Expression 159 8.5 Further Reading and Advanced Topics 160 A Pseudocode for the SMO Algorithm 162 B Background Mathematics 165 B.I Vector Spaces 165 B.2 Inner Product Spaces 167 B.3 Hilbert Spaces 169 B.4 Operators, ...
... 157 8.4.2 Gene Expression 159 8.5 Further Reading and Advanced Topics 160 A Pseudocode for the SMO Algorithm 162 B Background Mathematics 165 B.I Vector Spaces 165 B.2 Inner Product Spaces 167 B.3 Hilbert Spaces 169 B.4 Operators, ...
Sivu 1
... and for each of them machine learning algorithms could provide 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 ...
... and for each of them machine learning algorithms could provide 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 ...
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 3
We discussed how the quality of an on-line learning algorithm can be assessed in terms of the number of mistakes it makes during the ... Early machine learning algorithms aimed to learn representations of simple symbolic functions that ...
We discussed how the quality of an on-line learning algorithm can be assessed in terms of the number of mistakes it makes during the ... Early machine learning algorithms aimed to learn representations of simple symbolic functions that ...
Sivu 4
The generalisation criterion places an altogether different constraint on the learning algorithm. This is most amply illustrated by the extreme case of rote learning. Many classical algorithms of machine learning are capable of ...
The generalisation criterion places an altogether different constraint on the learning algorithm. This is most amply illustrated by the extreme case of rote learning. Many classical algorithms of machine learning are capable of ...
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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 |