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 61
Sivu 7
... training sets with hundreds of thousands of examples, while the compact representation of the hypothesis means that ... Learning 7 Support Vector Machines for Learning Exercises.
... training sets with hundreds of thousands of examples, while the compact representation of the hypothesis means that ... Learning 7 Support Vector Machines for Learning Exercises.
Sivu 9
Nello Cristianini, John Shawe-Taylor. Linear. Learning. Machines. In supervised learning, the learning machine is given a training set of examples (or inputs) with associated labels (or output values). Usually the examples are in the form ...
Nello Cristianini, John Shawe-Taylor. Linear. Learning. Machines. In supervised learning, the learning machine is given a training set of examples (or inputs) with associated labels (or output values). Usually the examples are in the form ...
Sivu 10
... learning from examples, we first introduce some notation we will be using throughout the book to refer to inputs, outputs. training sets, and so on. Definition 2.1 We typically use X to denote the input. 10 2 Linear Learning Machines.
... learning from examples, we first introduce some notation we will be using throughout the book to refer to inputs, outputs. training sets, and so on. Definition 2.1 We typically use X to denote the input. 10 2 Linear Learning Machines.
Sivu 11
... training examples, which are also called training data. It is usually denoted by where / is the number of examples. We refer to the x, as examples or instances and the y, as their labels. The training set S is trivial if the labels of ...
... training examples, which are also called training data. It is usually denoted by where / is the number of examples. We refer to the x, as examples or instances and the y, as their labels. The training set S is trivial if the labels of ...
Sivu 12
... training set S is the distribution of the margins of the examples in S. We sometimes refer to the minimum of the margin distribution as the (functional) margin of a hyperplane (w, b) with respect to a training set S. In both definitions ...
... training set S is the distribution of the margins of the examples in S. We sometimes refer to the minimum of the margin distribution as the (functional) margin of a hyperplane (w, b) with respect to a training set S. In both definitions ...
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