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 22
Sivu 9
... iterative procedures and theoretical analysis of their generalisation properties, provide the framework within which the construction of more complex systems will be developed in the coming chapters. In this chapter we review results ...
... iterative procedures and theoretical analysis of their generalisation properties, provide the framework within which the construction of more complex systems will be developed in the coming chapters. In this chapter we review results ...
Sivu 11
... iterative algorithms optimising different cost functions were introduced in the 1960s for separating points from two populations by means of a hyperplane. In the following subsections we will review some of the best- known, and will ...
... iterative algorithms optimising different cost functions were introduced in the 1960s for separating points from two populations by means of a hyperplane. In the following subsections we will review some of the best- known, and will ...
Sivu 15
... iterations depends on the margin of the augmented (including bias) weight training set. This margin is always less than or equal to y and can be very much smaller. It will equal y when bopt = 0, and so the bound using the augmented ...
... iterations depends on the margin of the augmented (including bias) weight training set. This margin is always less than or equal to y and can be very much smaller. It will equal y when bopt = 0, and so the bound using the augmented ...
Sivu 17
... iteration of the for loop is that after a training example x, has been used to update the weight vector in the ... iterations. One could envisage an adaptation of the perceptron algorithm that would include these additional coordinates ...
... iteration of the for loop is that after a training example x, has been used to update the weight vector in the ... iterations. One could envisage an adaptation of the perceptron algorithm that would include these additional coordinates ...
Sivu 19
... iterative algorithm similar to the perceptron algorithm exists that is guaranteed to converge to the maximal margin solution. We will briefly analyse this algorithm in Chapter 7. The perceptron algorithm is guaranteed to converge only ...
... iterative algorithm similar to the perceptron algorithm exists that is guaranteed to converge to the maximal margin solution. We will briefly analyse this algorithm in Chapter 7. The perceptron algorithm is guaranteed to converge only ...
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