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 70
Sivu x
... definitions on their way to using SVMs. We believe that by developing the material in intuitively appealing but rigorous stages, in fact SVMs appear as simple and natural systems. Where possible we first introduce concepts in a simple ...
... definitions on their way to using SVMs. We believe that by developing the material in intuitively appealing but rigorous stages, in fact SVMs appear as simple and natural systems. Where possible we first introduce concepts in a simple ...
Sivu 1
... defined. The availability of reliable learning systems is of strategic importance, as there are many tasks that cannot be solved by classical programming techniques, since no mathematical model of the problem is available. So for ...
... defined. The availability of reliable learning systems is of strategic importance, as there are many tasks that cannot be solved by classical programming techniques, since no mathematical model of the problem is available. So for ...
Sivu 4
... definition of these concepts will be given in Chapter 4, when we will motivate the particular models we shall be using. 1.3. Improving. Generalisation. The generalisation criterion places an altogether different constraint on the learning ...
... definition of these concepts will be given in Chapter 4, when we will motivate the particular models we shall be using. 1.3. Improving. Generalisation. The generalisation criterion places an altogether different constraint on the learning ...
Sivu 7
... define the implicit feature space in which the linear learning machines operate. The use of kernel functions is the key to the efficient use of high dimensional feature spaces. The danger of overfitting inherent in high dimensions ...
... define the implicit feature space in which the linear learning machines operate. The use of kernel functions is the key to the efficient use of high dimensional feature spaces. The danger of overfitting inherent in high dimensions ...
Sivu 10
... defined by the equation (w - \) + b = 0 (see Figure 2.1). A hyperplane is an affine subspace of dimension n — 1 which divides the space into two half ... Definition 2.1 We typically use X to denote the input. 10 2 Linear Learning Machines.
... defined by the equation (w - \) + b = 0 (see Figure 2.1). A hyperplane is an affine subspace of dimension n — 1 which divides the space into two half ... Definition 2.1 We typically use X to denote the input. 10 2 Linear Learning Machines.
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