fMRI analysis via one-class machine learning techniques

David R. Hardoon; Larry M. Manevitz

fMRI analysis via one-class machine learning techniques

Research output: Contribution to journal › Conference article › peer-review

15 Scopus citations

Abstract

We show how one-class compression Neural Networks and one-class SVM can be applied to fMRI data to learn the classification of brain activity associated with a specific motor activity. For comparison purposes, we use two labeled data and see what degree of classification ability is lost compared with the usual two-class SVM.

Original language	English
Pages (from-to)	1604-1605
Number of pages	2
Journal	IJCAI International Joint Conference on Artificial Intelligence
State	Published - 2005
Externally published	Yes
Event	19th International Joint Conference on Artificial Intelligence, IJCAI 2005 - Edinburgh, United Kingdom Duration: 30 Jul 2005 → 5 Aug 2005

Cite this

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title = "fMRI analysis via one-class machine learning techniques",

abstract = "We show how one-class compression Neural Networks and one-class SVM can be applied to fMRI data to learn the classification of brain activity associated with a specific motor activity. For comparison purposes, we use two labeled data and see what degree of classification ability is lost compared with the usual two-class SVM.",

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AB - We show how one-class compression Neural Networks and one-class SVM can be applied to fMRI data to learn the classification of brain activity associated with a specific motor activity. For comparison purposes, we use two labeled data and see what degree of classification ability is lost compared with the usual two-class SVM.

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T2 - 19th International Joint Conference on Artificial Intelligence, IJCAI 2005

Y2 - 30 July 2005 through 5 August 2005

ER -

fMRI analysis via one-class machine learning techniques

Abstract

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