Skip to main content Accessibility help
×
Hostname: page-component-7c8c6479df-fqc5m Total loading time: 0 Render date: 2024-03-28T17:49:32.267Z Has data issue: false hasContentIssue false

Appendix B - kNN, PNN, and Bayes classifiers

from Part VIII - Appendices

Published online by Cambridge University Press:  05 July 2014

S. Y. Kung
Affiliation:
Princeton University, New Jersey
Get access

Summary

There are two main learning strategies, namely inductive learning and transductive learning. These strategies are differentiated by their different ways of treating the (distribution of) testing data. The former adopts off-line learning models, see Figure B.1(a), but the latter usually adopts online learning models, see Figure B.1(b).

  • Inductive learning strategies. The decision rule, trained under an inductive setting, must cover all the possible data in the entire vector space. More explicitly, the discriminant function f(w, x) can be trained by inductive learning. This approach can effectively distill the information inherent in the training dataset off-line into a simple set of decision parameters w, thus enjoying the advantage of having a low classification complexity. As shown in Figure B.1(a), this approach contains two stages: (1) an off-line learning phase and (2) an on-field prediction phase. During the learning phase, the training dataset is used to learn the decision parameter w, which dictates the decision boundary: f(w, x) = 0. In the prediction phase, no more learning is required, so the decision making can be made on-the-fly with minimum latency.

  • Transductive learning strategies. In this case, the learner may explicitly make use of the structure and/or location of the putative test dataset in the decision process [281]. Hence, the discriminant function can be tailored to the specific test sample after it has been made known, presumably improving the prediction accuracy.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2014

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • kNN, PNN, and Bayes classifiers
  • S. Y. Kung, Princeton University, New Jersey
  • Book: Kernel Methods and Machine Learning
  • Online publication: 05 July 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139176224.026
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • kNN, PNN, and Bayes classifiers
  • S. Y. Kung, Princeton University, New Jersey
  • Book: Kernel Methods and Machine Learning
  • Online publication: 05 July 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139176224.026
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • kNN, PNN, and Bayes classifiers
  • S. Y. Kung, Princeton University, New Jersey
  • Book: Kernel Methods and Machine Learning
  • Online publication: 05 July 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139176224.026
Available formats
×