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. |
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Tulokset 1 - 5 kokonaismäärästä 45
Sivu ix
... years there have been very significant developments in the theoretical understanding of Support Vector Machines (SVMs) as well as algorithmic strategies for implementing them, and applications of the approach to practical problems.
... years there have been very significant developments in the theoretical understanding of Support Vector Machines (SVMs) as well as algorithmic strategies for implementing them, and applications of the approach to practical problems.
Sivu 1
We will refer to this approach to problem solving as the learning methodology The same reasoning applies to the problem of finding genes in a DNA sequence, filtering email, detecting or recognising objects in machine vision, and so on.
We will refer to this approach to problem solving as the learning methodology The same reasoning applies to the problem of finding genes in a DNA sequence, filtering email, detecting or recognising objects in machine vision, and so on.
Sivu 2
These tasks cannot be solved by a traditional programming approach since the system designer cannot precisely specify the method by which the correct output can be computed from the input data. An alternative strategy for solving this ...
These tasks cannot be solved by a traditional programming approach since the system designer cannot precisely specify the method by which the correct output can be computed from the input data. An alternative strategy for solving this ...
Sivu 4
There is, however, a more fundamental problem with this approach in that even when we can find a hypothesis that is consistent with the training data, it may not make correct classifications of unseen data. The ability of a hypothesis ...
There is, however, a more fundamental problem with this approach in that even when we can find a hypothesis that is consistent with the training data, it may not make correct classifications of unseen data. The ability of a hypothesis ...
Sivu 5
The approach that we will adopt is to motivate the trade-off by reference to statistical bounds on the generalisation error. These bounds will typically depend on certain quantities such as the margin of the classifier, ...
The approach that we will adopt is to motivate the trade-off by reference to statistical bounds on the generalisation error. These bounds will typically depend on certain quantities such as the margin of the classifier, ...
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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 |