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 79
Sivu 5
... algorithms which optimise the particular measure. The drawback of such an approach is that the algorithm is only as good as the result that motivates it. On the other hand the strength is that the statistical result provides a well ...
... algorithms which optimise the particular measure. The drawback of such an approach is that the algorithm is only as good as the result that motivates it. On the other hand the strength is that the statistical result provides a well ...
Sivu 6
... algorithm for learning the input/output mapping. Finally, there is the attraction of discovering insights into the way that humans function, an attraction that so inspired early work in neural networks, occasionally to the detriment of ...
... algorithm for learning the input/output mapping. Finally, there is the attraction of discovering insights into the way that humans function, an attraction that so inspired early work in neural networks, occasionally to the detriment of ...
Sivu 7
... algorithm from optimisation theory that implements a learning bias derived from statistical learning theory. This learning strategy introduced by Vapnik and co-workers is a principled and very powerful method that in the few years since ...
... algorithm from optimisation theory that implements a learning bias derived from statistical learning theory. This learning strategy introduced by Vapnik and co-workers is a principled and very powerful method that in the few years since ...
Sivu 8
... algorithms became an important sub field of artificial intelligence, eventually forming the separate subject area of machine learning. A very readable 'first introduction' to many problems in machine learning is provided by Tom ...
... algorithms became an important sub field of artificial intelligence, eventually forming the separate subject area of machine learning. A very readable 'first introduction' to many problems in machine learning is provided by Tom ...
Sivu 9
... algorithms and issues of classification, and then we will move on to the problem of regression. Throughout this book, we will refer to learning machines using hypotheses that form linear combinations of the input variables as linear ...
... algorithms and issues of classification, and then we will move on to the problem of regression. Throughout this book, we will refer to learning machines using hypotheses that form linear combinations of the input variables as linear ...
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