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 95
Sivu xiii
... and output space xeX input and input space F feature space ? general class of real-valued functions if class of linear functions <x-z> inner product between x and z <t>:X ^F mapping to feature space K(x,z) kernel {<f>(x) - <j)(z)) ...
... and output space xeX input and input space F feature space ? general class of real-valued functions if class of linear functions <x-z> inner product between x and z <t>:X ^F mapping to feature space K(x,z) kernel {<f>(x) - <j)(z)) ...
Sivu 2
The input/output pairings typically reflect a functional relationship mapping inputs to outputs, though this is not always the case as for example when the outputs are corrupted by noise. When an underlying function from inputs to ...
The input/output pairings typically reflect a functional relationship mapping inputs to outputs, though this is not always the case as for example when the outputs are corrupted by noise. When an underlying function from inputs to ...
Sivu 4
The second problem is that frequently training data are noisy and so there is no guarantee that there is an underlying function which correctly maps the training data. The example of credit checking is clearly in this category since the ...
The second problem is that frequently training data are noisy and so there is no guarantee that there is an underlying function which correctly maps the training data. The example of credit checking is clearly in this category since the ...
Sivu 5
As an example the Minimum Description Length (MDL) principle proposes to use the set of hypotheses for which the description of the chosen function together with the list of training errors is shortest. The approach that we will adopt ...
As an example the Minimum Description Length (MDL) principle proposes to use the set of hypotheses for which the description of the chosen function together with the list of training errors is shortest. The approach that we will adopt ...
Sivu 6
possible functions from the input space to the output domain), then learning is impossible since no amount of training data will tell us how to classify unseen examples. Problems also arise if we allow ourselves the freedom of choosing ...
possible functions from the input space to the output domain), then learning is impossible since no amount of training data will tell us how to classify unseen examples. Problems also arise if we allow ourselves the freedom of choosing ...
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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 |