An Introduction to Support Vector Machines and Other Kernel-based Learning MethodsCambridge University Press, 23 Mar 2000 - 189 halaman 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. |
Isi
The Learning Methodology | 1 |
Linear Learning Machines | 9 |
KernelInduced Feature Spaces | 26 |
Generalisation Theory | 52 |
Optimisation Theory | 79 |
Support Vector Machines | 93 |
Edisi yang lain - Lihat semua
An Introduction to Support Vector Machines and Other Kernel-based Learning ... Nello Cristianini,John Shawe-Taylor Pratinjau tidak tersedia - 2000 |
Istilah dan frasa umum
1-norm soft margin a₁ algorithm analysis applications approach Bayesian bound Chapter classification computational consider constraints convergence convex corresponding datasets defined Definition described dual problem dual representation err(h 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 norm objective function 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 x₁ zero Σα