An Introduction to Support Vector Machines and Other Kernel-based Learning MethodsCambridge University Press, 23.3.2000 - 189 sivua This is the first comprehensive introduction to Support Vector Machines (SVMs), a new 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. |
Sisältö
The Learning Methodology | 1 |
Linear Learning Machines | 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 |
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 applications approach Bayesian bound Chapter classification computational consider constraints convergence convex corresponding datasets defined Definition described dual problem dual representation feasibility gap feature space finite Gaussian processes generalisation error geometric margin given gradient Hence heuristics high dimensional Hilbert space hyperplane hypothesis inner product space input space iterative K(xi Karush-Kuhn-Tucker Karush-Kuhn-Tucker conditions kernel function Lagrange multipliers Lagrangian learning algorithm linear functions linear learning machines loss function machine learning margin slack vector maximal margin hyperplane maximise minimise neural networks new,unc norm objective function obtained on-line optimal optimisation problem output parameters perceptron perceptron algorithm performance positive semi-definite Remark result ridge regression Section sequence slack variables soft margin optimisation solution subset Support Vector Machines SVMs techniques Theorem training data training examples training points training set update Vapnik VC dimension weight vector zero