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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Tulokset 1 - 5 kokonaismäärästä 70
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The fundamental principle that guided the writing of the book is that it should be accessible to students and practitioners who would prefer to avoid complicated proofs and definitions on their way to using SVMs.
The fundamental principle that guided the writing of the book is that it should be accessible to students and practitioners who would prefer to avoid complicated proofs and definitions on their way to using SVMs.
Sivu 1
They have demonstrated that machines can display a significant level of learning ability, though the boundaries of this ability are far from being clearly defined. The availability of reliable learning systems is of strategic importance ...
They have demonstrated that machines can display a significant level of learning ability, though the boundaries of this ability are far from being clearly defined. The availability of reliable learning systems is of strategic importance ...
Sivu 4
... for the time being the change can be regarded as a move from symbolic to subsymbolic representations. A precise definition of these concepts will be given in Chapter 4, when we will motivate the particular models we shall be using.
... for the time being the change can be regarded as a move from symbolic to subsymbolic representations. A precise definition of these concepts will be given in Chapter 4, when we will motivate the particular models we shall be using.
Sivu 7
Chapter 3 deals with kernel functions which are used to 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.
Chapter 3 deals with kernel functions which are used to 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.
Sivu 10
A geometric interpretation of this kind of hypothesis is that the input space X is split into two parts by the hyperplane defined by the equation (w - \) + b = 0 (see Figure 2.1). A hyperplane is an affine subspace of dimension n — 1 ...
A geometric interpretation of this kind of hypothesis is that the input space X is split into two parts by the hyperplane defined by the equation (w - \) + b = 0 (see Figure 2.1). A hyperplane is an affine subspace of dimension n — 1 ...
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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 |