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 21
Sivu 8
... while Frank Rosenblatt's perceptron [122] contained many of the features of the systems discussed in the next chapter. ... The development of learning algorithms became an important sub field of artificial intelligence, ...
... while Frank Rosenblatt's perceptron [122] contained many of the features of the systems discussed in the next chapter. ... The development of learning algorithms became an important sub field of artificial intelligence, ...
Sivu 11
Several simple iterative algorithms optimising different cost functions were introduced in the 1960s for separating ... 2.1.1 Rosenblatt's Perceptron The first iterative algorithm for learning linear classifications is the procedure ...
Several simple iterative algorithms optimising different cost functions were introduced in the 1960s for separating ... 2.1.1 Rosenblatt's Perceptron The first iterative algorithm for learning linear classifications is the procedure ...
Sivu 12
Table 2.1: The Perceptron Algorithm (primal form) Note that y, > 0 implies correct classification of (x„>',). The (functional) margin distribution of a hyperplane (w, b) with respect to a training set S is the distribution of the ...
Table 2.1: The Perceptron Algorithm (primal form) Note that y, > 0 implies correct classification of (x„>',). The (functional) margin distribution of a hyperplane (w, b) with respect to a training set S is the distribution of the ...
Sivu 15
However, the bias is updated in the perceptron algorithm and if the standard update is made (without the R2 factor) the number of iterations depends on the margin of the augmented (including bias) weight training set.
However, the bias is updated in the perceptron algorithm and if the standard update is made (without the R2 factor) the number of iterations depends on the margin of the augmented (including bias) weight training set.
Sivu 16
... of the perceptron algorithm of Table 2.1 on S is bounded by Proof The proof defines an extended input space parametrised by A in which there is a hyperplane with margin y that has the same functionality as (w',b) on unseen data.
... of the perceptron algorithm of Table 2.1 on S is bounded by Proof The proof defines an extended input space parametrised by A in which there is a hyperplane with margin y that has the same functionality as (w',b) on unseen data.
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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 |