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 36
Sivu 5
These bounds will typically depend on certain quantities such as the margin of the classifier, and hence motivate ... The starting point for Bayesian analysis is a prior distribution over the set of hypotheses that describes the ...
These bounds will typically depend on certain quantities such as the margin of the classifier, and hence motivate ... The starting point for Bayesian analysis is a prior distribution over the set of hypotheses that describes the ...
Sivu 9
The important notions of margin and margin distribution are also introduced in this chapter. The classification results are all introduced for the binary or two-class case, and at the end of the chapter it is shown how to generalise ...
The important notions of margin and margin distribution are also introduced in this chapter. The classification results are all introduced for the binary or two-class case, and at the end of the chapter it is shown how to generalise ...
Sivu 12
The (functional) margin distribution of a hyperplane (w, b) with respect to a training set S is the distribution of the margins of the examples in S. We sometimes refer to the minimum of the margin distribution as the (functional) ...
The (functional) margin distribution of a hyperplane (w, b) with respect to a training set S is the distribution of the margins of the examples in S. We sometimes refer to the minimum of the margin distribution as the (functional) ...
Sivu 15
However, a theorem similar to NovikofTs exists, bounding the number of errors made during one iteration. It uses a different measure of the margin distribution, a measure that will play an important role in later chapters.
However, a theorem similar to NovikofTs exists, bounding the number of errors made during one iteration. It uses a different measure of the margin distribution, a measure that will play an important role in later chapters.
Sivu 19
algorithm we have already found many of the important concepts that will be used in the theory of Support Vector Machines: the margin, the margin distribution, and the dual representation. Remark 2.10 Since the number of updates equals ...
algorithm we have already found many of the important concepts that will be used in the theory of Support Vector Machines: the margin, the margin distribution, and the dual representation. Remark 2.10 Since the number of updates equals ...
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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 |