The Resource Conformal prediction for reliable machine learning : theory, adaptations, and applications, edited by Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk, (electronic book)
Conformal prediction for reliable machine learning : theory, adaptations, and applications, edited by Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk, (electronic book)
Resource Information
The item Conformal prediction for reliable machine learning : theory, adaptations, and applications, edited by Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk, (electronic book) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Sydney Jones Library, University of Liverpool.This item is available to borrow from 1 library branch.
Resource Information
The item Conformal prediction for reliable machine learning : theory, adaptations, and applications, edited by Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk, (electronic book) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Sydney Jones Library, University of Liverpool.
This item is available to borrow from 1 library branch.
- Summary
- "Traditional, low-dimensional, small scale data have been successfully dealt with using conventional software engineering and classical statistical methods, such as discriminant analysis, neural networks, genetic algorithms and others. But the change of scale in data collection and the dimensionality of modern data sets has profound implications on the type of analysis that can be done. Recently several kernel-based machine learning algorithms have been developed for dealing with high-dimensional problems, where a large number of features could cause a combinatorial explosion. These methods are quickly gaining popularity, and it is widely believed that they will help to meet the challenge of analysing very large data sets. Learning machines often perform well in a wide range of applications and have nice theoretical properties without requiring any parametric statistical assumption about the source of data (unlike traditional statistical techniques). However, a typical drawback of many machine learning algorithms is that they usually do not provide any useful measure of con dence in the predicted labels of new, unclassi ed examples. Con dence estimation is a well-studied area of both parametric and non-parametric statistics; however, usually only low-dimensional problems are considered"--
- Language
- eng
- Label
- Conformal prediction for reliable machine learning : theory, adaptations, and applications
- Title
- Conformal prediction for reliable machine learning
- Title remainder
- theory, adaptations, and applications
- Statement of responsibility
- edited by Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk
- Language
- eng
- Summary
- "Traditional, low-dimensional, small scale data have been successfully dealt with using conventional software engineering and classical statistical methods, such as discriminant analysis, neural networks, genetic algorithms and others. But the change of scale in data collection and the dimensionality of modern data sets has profound implications on the type of analysis that can be done. Recently several kernel-based machine learning algorithms have been developed for dealing with high-dimensional problems, where a large number of features could cause a combinatorial explosion. These methods are quickly gaining popularity, and it is widely believed that they will help to meet the challenge of analysing very large data sets. Learning machines often perform well in a wide range of applications and have nice theoretical properties without requiring any parametric statistical assumption about the source of data (unlike traditional statistical techniques). However, a typical drawback of many machine learning algorithms is that they usually do not provide any useful measure of con dence in the predicted labels of new, unclassi ed examples. Con dence estimation is a well-studied area of both parametric and non-parametric statistics; however, usually only low-dimensional problems are considered"--
- Assigning source
- Provided by publisher
- Cataloging source
- IDEBK
- Dewey number
- 006.3/1
- Index
- index present
- LC call number
- Q325.5
- LC item number
- C668 2014eb
- Literary form
- non fiction
- Nature of contents
-
- dictionaries
- bibliography
- http://library.link/vocab/relatedWorkOrContributorDate
- 1960-
- http://library.link/vocab/relatedWorkOrContributorName
-
- Balasubramanian, Vineeth
- Ho, Shen-Shyang
- Vovk, Vladimir
- http://library.link/vocab/subjectName
- Machine learning
- Label
- Conformal prediction for reliable machine learning : theory, adaptations, and applications, edited by Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk, (electronic book)
- Antecedent source
- unknown
- Bibliography note
- Includes bibliographical references and index
- Color
- multicolored
- Control code
- SCIDI878922864
- Dimensions
- unknown
- Extent
- 1 online resource
- File format
- unknown
- Form of item
- online
- Isbn
- 9780124017153
- Level of compression
- unknown
- Quality assurance targets
- not applicable
- Reformatting quality
- unknown
- Sound
- unknown sound
- Specific material designation
- remote
- Label
- Conformal prediction for reliable machine learning : theory, adaptations, and applications, edited by Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk, (electronic book)
- Antecedent source
- unknown
- Bibliography note
- Includes bibliographical references and index
- Color
- multicolored
- Control code
- SCIDI878922864
- Dimensions
- unknown
- Extent
- 1 online resource
- File format
- unknown
- Form of item
- online
- Isbn
- 9780124017153
- Level of compression
- unknown
- Quality assurance targets
- not applicable
- Reformatting quality
- unknown
- Sound
- unknown sound
- Specific material designation
- remote
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.liverpool.ac.uk/portal/Conformal-prediction-for-reliable-machine/8Pcd_-bPOFY/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.liverpool.ac.uk/portal/Conformal-prediction-for-reliable-machine/8Pcd_-bPOFY/">Conformal prediction for reliable machine learning : theory, adaptations, and applications, edited by Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk, (electronic book)</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.liverpool.ac.uk/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.liverpool.ac.uk/">Sydney Jones Library, University of Liverpool</a></span></span></span></span></div>
