An Introduction to Support Vector Machines and Other Kernel-based Learning MethodsThis 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. |
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Sivu i
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 data mining.
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 data mining.
Sivu ix
We believe that the topic has reached the point at which it should perhaps be viewed as its own subfield of machine learning, a subfield which promises much in both theoretical insights and practical usefulness.
We believe that the topic has reached the point at which it should perhaps be viewed as its own subfield of machine learning, a subfield which promises much in both theoretical insights and practical usefulness.
Sivu 1
The construction of machines capable of learning from experience has for a long time been the object of both philosophical and technical debate. The technical aspect of the debate has received an enormous impetus from the advent of ...
The construction of machines capable of learning from experience has for a long time been the object of both philosophical and technical debate. The technical aspect of the debate has received an enormous impetus from the advent of ...
Sivu 3
The study of how this affects the learner's ability to learn different tasks is known as query learning. Further complexities of interaction are considered in reinforcement learning, where the learner has a range of actions at their ...
The study of how this affects the learner's ability to learn different tasks is known as query learning. Further complexities of interaction are considered in reinforcement learning, where the learner has a range of actions at their ...
Sivu 4
The type of data that is of interest to machine learning practitioners is increasingly of these two types, hence rendering the proposed measure of quality difficult to implement. There is, however, a more fundamental problem with this ...
The type of data that is of interest to machine learning practitioners is increasingly of these two types, hence rendering the proposed measure of quality difficult to implement. There is, however, a more fundamental problem with this ...
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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 |
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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
Kirjaluettelon tiedot
| Otsikko | An Introduction to Support Vector Machines and Other Kernel-based Learning Methods |
| Kirjailijat | Nello Cristianini, John Shawe-Taylor |
| Kustantaja | Cambridge University Press, 2000 |
| ISBN | 1139643630, 9781139643634 |
| Vie sitaatti | BiBTeX EndNote RefMan |