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 37
Sivu i
SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequence analysis, etc. Their first introduction in the early '90s led to an ...
SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequence analysis, etc. Their first introduction in the early '90s led to an ...
Sivu vi
... Generalisation and Luckiness 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 ...
... Generalisation and Luckiness 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 ...
Sivu 5
It is only by performing a principled analysis of the computational complexity that we can avoid settling for heuristics that work well on small examples, but break down once larger training sets are used. The theory of computational ...
It is only by performing a principled analysis of the computational complexity that we can avoid settling for heuristics that work well on small examples, but break down once larger training sets are used. The theory of computational ...
Sivu 6
There are, however, many difficulties inherent in the learning methodology, difficulties that deserve careful study and analysis. One example is the choice of the class of functions from which the input/output mapping must be sought.
There are, however, many difficulties inherent in the learning methodology, difficulties that deserve careful study and analysis. One example is the choice of the class of functions from which the input/output mapping must be sought.
Sivu 8
Gauss proposed the idea of least squares regression in the 18th century, while Fisher's approach [40] to classification in the 1930s still provides the starting point for most analysis and methods. Researchers in the area of artificial ...
Gauss proposed the idea of least squares regression in the 18th century, while Fisher's approach [40] to classification in the 1930s still provides the starting point for most analysis and methods. Researchers in the area of artificial ...
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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 |