An Introduction to Support Vector Machines and Other Kernel-based Learning MethodsCambridge University Press, 23.3.2000 This 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 26
Sivu xiii
... logarithm to the base 2 x', X' transpose of vector, matrix N, R natural, real numbers S training sample / training set size '/ learning rate E error probability <> confidence y margin c slack variables d VC dimension Notation.
... logarithm to the base 2 x', X' transpose of vector, matrix N, R natural, real numbers S training sample / training set size '/ learning rate E error probability <> confidence y margin c slack variables d VC dimension Notation.
Sivu 15
... margin of the augmented (including bias) weight training set. This margin is ... vector. The resulting value of the norm is inversely proportional to the margin ... slack variable of an example (x,,y,) with respect to the hyperplane (w, b) ...
... margin of the augmented (including bias) weight training set. This margin is ... vector. The resulting value of the norm is inversely proportional to the margin ... slack variable of an example (x,,y,) with respect to the hyperplane (w, b) ...
Sivu 16
... margin y, and takes into account any misclassifications of the training data. Figure 2.4 shows the size of the margin slack variables for two misclassified points for a hyperplane with unit norm. All of the other points in the figure ...
... margin y, and takes into account any misclassifications of the training data. Figure 2.4 shows the size of the margin slack variables for two misclassified points for a hyperplane with unit norm. All of the other points in the figure ...
Sivu 65
Katseluoikeutesi tähän teokseen on päättynyt.
Katseluoikeutesi tähän teokseen on päättynyt.
Sivu 66
Katseluoikeutesi tähän teokseen on päättynyt.
Katseluoikeutesi tähän teokseen on päättynyt.
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 |
Muita painoksia - Näytä kaikki
An Introduction to Support Vector Machines and Other Kernel-based Learning ... Nello Cristianini,John Shawe-Taylor Rajoitettu esikatselu - 2000 |
An Introduction to Support Vector Machines and Other Kernel-based Learning ... Nello Cristianini,John Shawe-Taylor Esikatselu ei käytettävissä - 2000 |
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