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 53
Sivu 5
... of hypotheses for which the description of the chosen function together with the list of training errors is shortest. ... The starting point for Bayesian analysis is a prior distribution over the set of hypotheses that describes the ...
... of hypotheses for which the description of the chosen function together with the list of training errors is shortest. ... The starting point for Bayesian analysis is a prior distribution over the set of hypotheses that describes the ...
Sivu 11
The training set S is trivial if the labels of all the examples are equal. ... iterative algorithms optimising different cost functions were introduced in the 1960s for separating points from two populations by means of a hyperplane.
The training set S is trivial if the labels of all the examples are equal. ... iterative algorithms optimising different cost functions were introduced in the 1960s for separating points from two populations by means of a hyperplane.
Sivu 12
The (functional) margin distribution of a hyperplane (w, b) with respect to a training set S is the distribution of the ... which therefore measures the Euclidean distances of the points from the decision boundary in the input space.
The (functional) margin distribution of a hyperplane (w, b) with respect to a training set S is the distribution of the ... which therefore measures the Euclidean distances of the points from the decision boundary in the input space.
Sivu 13
Nello Cristianini, John Shawe-Taylor. Figure 2.2: The geometric margin of two points Figure 2.3: The margin of a training set. 2.1 Linear Classification 13.
Nello Cristianini, John Shawe-Taylor. Figure 2.2: The geometric margin of two points Figure 2.3: The margin of a training set. 2.1 Linear Classification 13.
Sivu 15
In contrast, for cases where |fooptl = 0(R), with R > 1, the bound obtained with the augmented training set will be a ... of the training sample, using the margins achieved by training points other than those closest to the hyperplane.
In contrast, for cases where |fooptl = 0(R), with R > 1, the bound obtained with the augmented training set will be a ... of the training sample, using the margins achieved by training points other than those closest to the hyperplane.
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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 |