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 48
Sivu 5
We distinguish between results that measure generalisation performance that can be obtained with a given finite number of training examples, and asymptotic results, which study how the generalisation behaves as the number of examples ...
We distinguish between results that measure generalisation performance that can be obtained with a given finite number of training examples, and asymptotic results, which study how the generalisation behaves as the number of examples ...
Sivu 8
You are given a set of correctly labelled mammals and fishes: [dog, cat, dolphin}, {goldfish, shark, tuna}. Taking the hypothesis space as the set of four functions obtained in Exercises 1 and 2, how would you pick the correct decision ...
You are given a set of correctly labelled mammals and fishes: [dog, cat, dolphin}, {goldfish, shark, tuna}. Taking the hypothesis space as the set of four functions obtained in Exercises 1 and 2, how would you pick the correct decision ...
Sivu 12
... margin by geometric margin we obtain the equivalent quantity for the normalised linear function ( TOf w' ir^f ^ ) , which therefore measures the Euclidean distances of the points from the decision boundary in the input space.
... margin by geometric margin we obtain the equivalent quantity for the normalised linear function ( TOf w' ir^f ^ ) , which therefore measures the Euclidean distances of the points from the decision boundary in the input space.
Sivu 15
In contrast, for cases where |fooptl = 0(R), with R > 1, the bound obtained with the augmented training set will be a factor 0(R2) worse than our bound. Remark 2.5 The critical quantity in the bound is the square of the ratio of the ...
In contrast, for cases where |fooptl = 0(R), with R > 1, the bound obtained with the augmented training set will be a factor 0(R2) worse than our bound. Remark 2.5 The critical quantity in the bound is the square of the ratio of the ...
Sivu 22
... is not of full rank, or in other situations where numerical stability problems occur, one can use the following solution: Table 2.3 : The Widrow-Hoff Algorithm (primal form) obtained by 22 2 Linear Learning Machines Ridge Regression.
... is not of full rank, or in other situations where numerical stability problems occur, one can use the following solution: Table 2.3 : The Widrow-Hoff Algorithm (primal form) obtained by 22 2 Linear Learning Machines Ridge Regression.
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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 |