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. |
Kirjan sisältä
Tulokset 1 - 5 kokonaismäärästä 46
Sivu xiii
N dimension of feature space yeY output and output space xeX input and input space F feature space ? general class of real-valued functions if class of linear functions <x-z> inner product between x and z <t>:X ^F mapping to feature ...
N dimension of feature space yeY output and output space xeX input and input space F feature space ? general class of real-valued functions if class of linear functions <x-z> inner product between x and z <t>:X ^F mapping to feature ...
Sivu 2
The solution is chosen from a set of candidate functions which map from the input space to the output domain. Usually we will choose a particular set or class of candidate functions known as hypotheses before we begin trying to learn ...
The solution is chosen from a set of candidate functions which map from the input space to the output domain. Usually we will choose a particular set or class of candidate functions known as hypotheses before we begin trying to learn ...
Sivu 5
For the first class there exist algorithms that run in time polynomial in the size of the input, while for the second the ... (that is all possible functions from the input space to the output domain), 1.3 Improving Generalisation 5.
For the first class there exist algorithms that run in time polynomial in the size of the input, while for the second the ... (that is all possible functions from the input space to the output domain), 1.3 Improving Generalisation 5.
Sivu 6
possible functions from the input space to the output domain), then learning is impossible since no amount of training data will tell us how to classify unseen examples. Problems also arise if we allow ourselves the freedom of choosing ...
possible functions from the input space to the output domain), then learning is impossible since no amount of training data will tell us how to classify unseen examples. Problems also arise if we allow ourselves the freedom of choosing ...
Sivu 7
What is the input space? How might the inputs be represented for computer processing of such data? Give an example of a function realising the classification rule. 2. Repeat Exercise 1 for the following categories of animals: 1.5 ...
What is the input space? How might the inputs be represented for computer processing of such data? Give an example of a function realising the classification rule. 2. Repeat Exercise 1 for the following categories of animals: 1.5 ...
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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 |