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 39
Sivu 2
Hence, we can view the choice of the set of hypotheses (or hypothesis space) as one of the key ingredients of the learning strategy. The algorithm which takes the training data as input and selects a hypothesis from the hypothesis space ...
Hence, we can view the choice of the set of hypotheses (or hypothesis space) as one of the key ingredients of the learning strategy. The algorithm which takes the training data as input and selects a hypothesis from the hypothesis space ...
Sivu 6
One example is the choice of the class of functions from which the input/output mapping must be sought. The class must be chosen to be sufficiently rich so that the required mapping or an approximation to it can be found, ...
One example is the choice of the class of functions from which the input/output mapping must be sought. The class must be chosen to be sufficiently rich so that the required mapping or an approximation to it can be found, ...
Sivu 7
The particular choice of a convex learning bias also results in the absence of local minima so that solutions can always be found efficiently even for training sets with hundreds of thousands of examples, while the compact ...
The particular choice of a convex learning bias also results in the absence of local minima so that solutions can always be found efficiently even for training sets with hundreds of thousands of examples, while the compact ...
Sivu 16
... parametrised by A in which there is a hyperplane with margin y that has the same functionality as (w',b) on unseen data. We can then apply Theorem 2.3 in the extended space. Finally, optimising the choice of A will give the result.
... parametrised by A in which there is a hyperplane with margin y that has the same functionality as (w',b) on unseen data. We can then apply Theorem 2.3 in the extended space. Finally, optimising the choice of A will give the result.
Sivu 17
optimising the choice of A will give the result. The extended input space has an extra coordinate for each training example. The new entries for example x, are all zero except for the value A in the /th additional coordinate.
optimising the choice of A will give the result. The extended input space has an extra coordinate for each training example. The new entries for example x, are all zero except for the value A in the /th additional coordinate.
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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 |