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 6 - 10 kokonaismäärästä 61
Sivu 5
... Chapter 4 we will describe in more detail the type of statistical result that will motivate our algorithms. We distinguish between results that measure generalisation performance that can be obtained with a given finite number of ...
... Chapter 4 we will describe in more detail the type of statistical result that will motivate our algorithms. We distinguish between results that measure generalisation performance that can be obtained with a given finite number of ...
Sivu 6
... Chapter 4. 1.4. Attractions. and. Drawbacks. of. Learning. It is not surprising that the promise of the learning methodology should be so tantalising. Firstly, the range of applications that can potentially be solved by such an approach is ...
... Chapter 4. 1.4. Attractions. and. Drawbacks. of. Learning. It is not surprising that the promise of the learning methodology should be so tantalising. Firstly, the range of applications that can potentially be solved by such an approach is ...
Sivu 7
... Chapters 2 and 3, the learning bias in Chapter 4, and the learning algorithm in Chapters 5 and 7. Chapter 6 is the key chapter in which all these components are brought together, while Chapter 8 gives an overview of some applications ...
... Chapters 2 and 3, the learning bias in Chapter 4, and the learning algorithm in Chapters 5 and 7. Chapter 6 is the key chapter in which all these components are brought together, while Chapter 8 gives an overview of some applications ...
Sivu 8
... chapter. In particular, the idea of modelling learning problems as problems of search in a suitable hypothesis space is characteristic of the artificial intelligence approach. Solomonoff also formally studied the problem of learning as ...
... chapter. In particular, the idea of modelling learning problems as problems of search in a suitable hypothesis space is characteristic of the artificial intelligence approach. Solomonoff also formally studied the problem of learning as ...
Sivu 9
... chapters. The important notions of margin and margin distribution are also introduced in this chapter. The classification results are all introduced for the binary or two-class case, and at the end of the chapter it is shown how to ...
... chapters. The important notions of margin and margin distribution are also introduced in this chapter. The classification results are all introduced for the binary or two-class case, and at the end of the chapter it is shown how to ...
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