Linked Data Explorerhttp://worldcat.org/entity/work/id/797260587
Learning with support vector machines
Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such a
Open All Close All
http://schema.org/about
- http://experiment.worldcat.org/entity/work/data/797260587#Topic/machines_a_vecteurs_supports
- http://experiment.worldcat.org/entity/work/data/797260587#Topic/kernel_functions
- http://experiment.worldcat.org/entity/work/data/797260587#Topic/support_vector_machines
- http://id.worldcat.org/fast/1747369
- http://experiment.worldcat.org/entity/work/data/797260587#Topic/bioinformatics
- http://experiment.worldcat.org/entity/work/data/797260587#Topic/computers_intelligence_ai_&_semantics
- http://id.loc.gov/authorities/subjects/sh2008009003
- http://experiment.worldcat.org/entity/work/data/797260587#Topic/computers_enterprise_applications_business_intelligence_tools
http://schema.org/description
- "Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such a"@en
- "Support Vectors Machines have become a well established tool within machine learning.They work well in practice and have now been used across a wide range of applications from recognizing handwritten digits, to face identification, text categorisation, bioinformatics and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise.We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data--[Source inconnue]."
- "Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing handwritten digits, to face identification, text categorisation, bioinformatics and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data."@en
- "Support vector machines have become a well-established tool within machine learning.They work well in practice and have now been used across a wide range of applications from recognizing handwritten digits, to face identification, text categorization, bioinformatics and database marketing. In this book we give an introductory overview of this subject. We start with a simple support vector machine for performing binary classification before considering multi-class classification and learning in the presence of noise.We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data."
http://schema.org/genre
- "Electronic books"@en