LINGUIST List 12.587

Fri Mar 2 2001

TOC: Artificial Intelligence Review Vol. 14

Editor for this issue: Naomi Ogasawara <naomilinguistlist.org>


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  1. kap-listman, Artificial Intelligence Review. Vol. 14, Issue 6, December 2000

Message 1: Artificial Intelligence Review. Vol. 14, Issue 6, December 2000

Date: Thu, 01 Mar 2001 02:17:37 +0100 (MET)
From: kap-listman <kap-listmanwkap.nl>
Subject: Artificial Intelligence Review. Vol. 14, Issue 6, December 2000



Artificial Intelligence Review
An International Science and Engineering Journal

ISSN 0269-2821

http://www.wkap.nl/issuetoc.htm/0269-2821+14+6+2000

Vol. 14, Issue 6, December 2000.

TITLE: About the Authors
AUTHOR(S): 
KEYWORD(S): customer retention, data mining, deviation analysis, feature
 selection, multiple level association rules.
PAGE(S): 3-8

TITLE: PlanMine- Predicting Plan Failures Using Sequence Mining
AUTHOR(S): Mohammed J. Zaki, Neal Lesh, Mitsunori Ogihara
KEYWORD(S): plan monitoring, predicting failures, sequence mining.
PAGE(S): 421-446

TITLE: Intelligent Data Analysis for Protein Disorder Prediction
AUTHOR(S): Pedro Romero, Zoran Obradovic, A. Keith Dunker
KEYWORD(S): data mining, neural networks, protein structure, protein
 disorder prediction, protein databases.
PAGE(S): 447-484

TITLE: Automated Cellular Modeling and Prediction on a Large Scale
AUTHOR(S): Piew Datta, Brij Masand, D. R. Mani, Bin Li
KEYWORD(S): churn prediction, neural networks, decision trees.
PAGE(S): 485-502

TITLE: Techniques and Experience in Mining Remotely Sensed Satellite Data
AUTHOR(S): Thomas H. Hinke, John Rushing, Heggere Ranganath, Sara J. Graves
KEYWORD(S): data mining, association rules, remotely sensed satellite data,
 texture mining, target independent mining.
PAGE(S): 503-531

TITLE: Adaptive Intrusion Detection- A Data Mining Approach
AUTHOR(S): Wenke Lee, Salvatore J. Stolfo, Kui W. Mok
KEYWORD(S): association rules, audit data, classification, feature 
 construction, frequent episodes, intrusion detection.
PAGE(S): 533-567

TITLE: Customer Retention via Data Mining
AUTHOR(S): KianSing Ng, Huan Liu
KEYWORD(S): customer retention, data mining, deviation analysis, feature
 selection, multiple level association rules.
PAGE(S): 569-590

TITLE: Language, Vision & Music- Workshop Report on The Eighth
 International Workshop on the Cognitive Science of Natural
 Language Processing (CSNLP-8) National University of Ireland,
 Galway (NUI Galway), Galway, Ireland Monday 9th-:Wednesday
 11th August, 1999
AUTHOR(S): Paul Mc Kevitt, Conn Mulvihill, Sean O Nuallain
KEYWORD(S): customer retention, data mining, deviation analysis, feature
 selection, multiple level association rules.
PAGE(S): 591-613

TITLE: Dov M. Gabbay, C.J. Hogger, J.A. Robinson (Eds.), Handbook
 of Logic in Artificial Intelligence and Logic Programming,
 Volume 2- Deduction Methodologies
AUTHOR(S): Gregor Erbach, Werner Saurer
PAGE(S): 615-617

TITLE: Shaogang Gong, Stephen J. McKenna, Alexandra Psarrou, Dynamic
 Vision: From Images to Face Recognition
AUTHOR(S): Jonathan Campbell
KEYWORD(S): customer retention, data mining, deviation analysis, feature
 selection, multiple level association rules.
PAGE(S): 619-621


 
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