Belief networks for cytogenetic image categorization

Boaz Lerner; Roy Malka

Belief networks for cytogenetic image categorization

Boaz Lerner
, Roy Malka

Department of Electrical & Computer Engineering

Research output: Contribution to conference › Paper › peer-review

Abstract

The structure and parameters of a belief network are learned in order to categorize cytogenetic images enabling the detection of genetic syndromes. We compare a structure learned from the data to another obtained utilizing expert knowledge and to the naive Bayesian classifier. We also study feature quantization needed for parameter learning in comparison to density estimation. Both networks achieve comparable accuracy for the cytogenetic database with a slight advantage to that based on expert knowledge.

Original language	English
Pages	297-300
Number of pages	4
State	Published - 1 Dec 2004
Event	2004 23rd IEEE Convention of Electrical and Electronics Engineers in Israel, Proceedings - Tel-Aviv, Israel Duration: 6 Sep 2004 → 7 Sep 2004

Conference

Conference	2004 23rd IEEE Convention of Electrical and Electronics Engineers in Israel, Proceedings
Country/Territory	Israel
City	Tel-Aviv
Period	6/09/04 → 7/09/04

ASJC Scopus subject areas

Electrical and Electronic Engineering
Electronic, Optical and Magnetic Materials

Cite this

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title = "Belief networks for cytogenetic image categorization",

abstract = "The structure and parameters of a belief network are learned in order to categorize cytogenetic images enabling the detection of genetic syndromes. We compare a structure learned from the data to another obtained utilizing expert knowledge and to the naive Bayesian classifier. We also study feature quantization needed for parameter learning in comparison to density estimation. Both networks achieve comparable accuracy for the cytogenetic database with a slight advantage to that based on expert knowledge.",

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year = "2004",

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language = "English",

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T1 - Belief networks for cytogenetic image categorization

AU - Lerner, Boaz

AU - Malka, Roy

PY - 2004/12/1

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N2 - The structure and parameters of a belief network are learned in order to categorize cytogenetic images enabling the detection of genetic syndromes. We compare a structure learned from the data to another obtained utilizing expert knowledge and to the naive Bayesian classifier. We also study feature quantization needed for parameter learning in comparison to density estimation. Both networks achieve comparable accuracy for the cytogenetic database with a slight advantage to that based on expert knowledge.

AB - The structure and parameters of a belief network are learned in order to categorize cytogenetic images enabling the detection of genetic syndromes. We compare a structure learned from the data to another obtained utilizing expert knowledge and to the naive Bayesian classifier. We also study feature quantization needed for parameter learning in comparison to density estimation. Both networks achieve comparable accuracy for the cytogenetic database with a slight advantage to that based on expert knowledge.

UR - https://www.scopus.com/pages/publications/17444385663

M3 - Paper

AN - SCOPUS:17444385663

SP - 297

EP - 300

T2 - 2004 23rd IEEE Convention of Electrical and Electronics Engineers in Israel, Proceedings

Y2 - 6 September 2004 through 7 September 2004

ER -

Belief networks for cytogenetic image categorization

Abstract

Conference

ASJC Scopus subject areas

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Cite this