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 37
Sivu i
... analysis, etc. Their first introduction in the early '90s led to an explosion of applications and deepening theoretical analysis, that has now established Support Vector Machines as one of the standard tools for machine learning and ...
... analysis, etc. Their first introduction in the early '90s led to an explosion of applications and deepening theoretical analysis, that has now established Support Vector Machines as one of the standard tools for machine learning and ...
Sivu vi
... Analysis of Learning 74 4.7 Exercises 76 4.8 Further Reading and Advanced Topics 76 5 Optimisation Theory 79 5.1 Problem Formulation 79 5.2 Lagrangian Theory 81 5.3 Duality 87 5.4 Exercises 89 5.5 Further Reading and Advanced Topics 90 ...
... Analysis of Learning 74 4.7 Exercises 76 4.8 Further Reading and Advanced Topics 76 5 Optimisation Theory 79 5.1 Problem Formulation 79 5.2 Lagrangian Theory 81 5.3 Duality 87 5.4 Exercises 89 5.5 Further Reading and Advanced Topics 90 ...
Sivu 5
... analysis of the computational complexity that we can avoid settling for heuristics that work well on small examples, but break down once larger training sets are used. The theory of computational complexity identifies two classes of ...
... analysis of the computational complexity that we can avoid settling for heuristics that work well on small examples, but break down once larger training sets are used. The theory of computational complexity identifies two classes of ...
Sivu 6
... analysis. One example is the choice of the class of functions from which the input/output mapping must be sought. The class must be chosen to be sufficiently rich so that the required mapping or an approximation to it can be found, but ...
... analysis. One example is the choice of the class of functions from which the input/output mapping must be sought. The class must be chosen to be sufficiently rich so that the required mapping or an approximation to it can be found, but ...
Sivu 8
... analysis and methods. Researchers in the area of artificial intelligence started to consider the problem of learning from its very beginning. Alan Turing [154] proposed the idea of learning machines in 1950, contesting the belief of ...
... analysis and methods. Researchers in the area of artificial intelligence started to consider the problem of learning from its very beginning. Alan Turing [154] proposed the idea of learning machines in 1950, contesting the belief of ...
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