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 65
Sivu xiii
... data e-insensitive loss function insensitive to errors less than e w weight vector b bias - dual variables or ... training set size '/ learning rate E error probability <> confidence y margin c slack variables d VC dimension Notation.
... data e-insensitive loss function insensitive to errors less than e w weight vector b bias - dual variables or ... training set size '/ learning rate E error probability <> confidence y margin c slack variables d VC dimension Notation.
Sivu 2
... set of inputs, or where that computation may be very expensive. Examples of ... training data. The input/output pairings typically reflect a functional ... learning, though binary classification is always considered first 2 1 The Learning ...
... set of inputs, or where that computation may be very expensive. Examples of ... training data. The input/output pairings typically reflect a functional ... learning, though binary classification is always considered first 2 1 The Learning ...
Sivu 4
... training data, it may not make correct classifications of unseen data. The ability of a hypothesis to correctly classify data not in the training set is known as its generalisation, and it is this property that we shall aim to optimise ...
... training data, it may not make correct classifications of unseen data. The ability of a hypothesis to correctly classify data not in the training set is known as its generalisation, and it is this property that we shall aim to optimise ...
Sivu 5
... set of hypotheses for which the description of the chosen function together with the list of training errors is shortest. The approach that we will adopt is to motivate the trade-off by reference to statistical bounds on the ...
... set of hypotheses for which the description of the chosen function together with the list of training errors is shortest. The approach that we will adopt is to motivate the trade-off by reference to statistical bounds on the ...
Sivu 6
... 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 the set of hypotheses after seeing the data, since we can simply ...
... 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 the set of hypotheses after seeing the data, since we can simply ...
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