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978-3-540-31933-7 |
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20221012195904.0 |
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100709s2005 gw | s |||| 0|eng d |
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|a 9783540319337
|9 978-3-540-31933-7
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|a 10.1007/b136140
|2 doi
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|a 005.74
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|a Active Mining
|h [electronic resource] :
|b Second International Workshop, AM 2003, Maebashi, Japan, October 28, 2003, Revised Selected Papers /
|c edited by Shusaku Tsumoto, Takahira Yamaguchi, Masayuki Numao, Hiroshi Motoda.
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|a 1st ed. 2005.
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg :
|b Imprint: Springer,
|c 2005.
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| 300 |
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|a XII, 348 p.
|b online resource.
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|a text
|b txt
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|a computer
|b c
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|a online resource
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|a text file
|b PDF
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|a Lecture Notes in Artificial Intelligence,
|x 2945-9141 ;
|v 3430
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| 505 |
0 |
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|a Overview -- Active Mining Project: Overview -- Tutorial Papers -- Computational and Statistical Methods in Bioinformatics -- Indexing and Mining Audiovisual Data -- Active Information Collection -- Relevance Feedback Document Retrieval Using Support Vector Machines -- Micro View and Macro View Approaches to Discovered Rule Filtering -- Mining Chemical Compound Structure Data Using Inductive Logic Programming -- First-Order Rule Mining by Using Graphs Created from Temporal Medical Data -- Active Data Mining -- Extracting Diagnostic Knowledge from Hepatitis Dataset by Decision Tree Graph-Based Induction -- Data Mining Oriented CRM Systems Based on MUSASHI: C-MUSASHI -- Investigation of Rule Interestingness in Medical Data Mining -- Experimental Evaluation of Time-Series Decision Tree -- Spiral Multi-aspect Hepatitis Data Mining -- Sentence Role Identification in Medline Abstracts: Training Classifier with Structured Abstracts -- CHASE 2 - Rule Based Chase Algorithm for Information Systems of Type ? -- Active User Reaction -- Empirical Comparison of Clustering Methods for Long Time-Series Databases -- Spiral Mining Using Attributes from 3D Molecular Structures -- Classification of Pharmacological Activity of Drugs Using Support Vector Machine -- Cooperative Scenario Mining from Blood Test Data of Hepatitis B and C -- Integrated Mining for Cancer Incidence Factors from Healthcare Data.
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| 650 |
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0 |
|a Database management.
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| 650 |
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0 |
|a Artificial intelligence.
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| 650 |
|
0 |
|a Algorithms.
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| 650 |
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0 |
|a Medical informatics.
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| 650 |
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0 |
|a Bioinformatics.
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| 650 |
1 |
4 |
|a Database Management.
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| 650 |
2 |
4 |
|a Artificial Intelligence.
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| 650 |
2 |
4 |
|a Algorithms.
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| 650 |
2 |
4 |
|a Health Informatics.
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| 650 |
2 |
4 |
|a Bioinformatics.
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| 700 |
1 |
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|a Tsumoto, Shusaku.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 700 |
1 |
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|a Yamaguchi, Takahira.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 700 |
1 |
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|a Numao, Masayuki.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 700 |
1 |
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|a Motoda, Hiroshi.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
0 |
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|t Springer Nature eBook
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| 776 |
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8 |
|i Printed edition:
|z 9783540811527
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| 776 |
0 |
8 |
|i Printed edition:
|z 9783540261575
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| 830 |
|
0 |
|a Lecture Notes in Artificial Intelligence,
|x 2945-9141 ;
|v 3430
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| 856 |
4 |
0 |
|u https://doi.uam.elogim.com/10.1007/b136140
|z Texto Completo
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| 912 |
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|a ZDB-2-SCS
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| 912 |
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|a ZDB-2-SXCS
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| 912 |
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|a ZDB-2-LNC
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| 950 |
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|a Computer Science (SpringerNature-11645)
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| 950 |
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|a Computer Science (R0) (SpringerNature-43710)
|
