Synopsis
Data Construction Method (DCM) based on the multiset division is proposed. The DCM can not only generate addition data within the domain value of the given sample for revealing the data's patterns, but also creates the membership function from the generated data for further applications. In this way, the DCM is taken to filling up the information gaps caused by small-sample-sets. To demonstrate the effectiveness of DCM, after presenting the DCM's theoretic background, properties, and algorithm, we compared the DCM with several existing approaches in estimating the population mean and improving the supervised neural network learning performance. The results show that the DCM performs better in a comparative manner. To show its applicability, we have applied the membership function derived from the DCM data to the studies of predicting the severe earthquakes in Taiwan and forecasting the psychotic episode of individual schizophrenics. The results have shown that the DCM can provide appropriate references for prediction from both spatial and temporal small data sets.
"synopsis" may belong to another edition of this title.
About the Author
Hsiao-Fan Wang is the Distinguished Chair Professor of National Tsing Hua University, Taiwan, ROC. She has been awarded the distinguished researcher of NSC in Taiwan and is the editor of several international journals. Her research interests are in MCDM, Fuzzy Set Theory, Rare and Huge Data Analysis and Green Value Chain Management.
"About this title" may belong to another edition of this title.
Search results for Data Construction Method for Small Sample Sets: Theory...
Seller Image
Data Construction Method for Small Sample Sets
Published by
LAP LAMBERT Academic Publishing Aug 2010, 2010
ISBN 10: 3838398378
ISBN 13: 9783838398372
New
Taschenbuch
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Data Construction Method (DCM) based on the multiset division is proposed. The DCM can not only generate addition data within the domain value of the given sample for revealing the data's patterns, but also creates the membership function from the generated data for further applications. In this way, the DCM is taken to filling up the information gaps caused by small-sample-sets. To demonstrate the effectiveness of DCM, after presenting the DCM's theoretic background, properties, and algorithm, we compared the DCM with several existing approaches in estimating the population mean and improving the supervised neural network learning performance. The results show that the DCM performs better in a comparative manner. To show its applicability, we have applied the membership function derived from the DCM data to the studies of predicting the severe earthquakes in Taiwan and forecasting the psychotic episode of individual schizophrenics. The results have shown that the DCM can provide appropriate references for prediction from both spatial and temporal small data sets. 172 pp. Englisch. Seller Inventory # 9783838398372
Seller Image
Data Construction Method for Small Sample Sets
Published by
LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838398378
ISBN 13: 9783838398372
New
Softcover
Seller: moluna, Greven, Germany
Seller rating 5 out of 5 stars
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Wang Hsiao-FanHsiao-Fan Wang is the Distinguished Chair Professor of National Tsing Hua University, Taiwan, ROC. She has been awarded the distinguished researcher of NSC in Taiwan and is the editor of several international journa. Seller Inventory # 5420050
Buy New
£ 49.25
£ 42.42 shipping
Ships from Germany to U.S.A.
Ships from Germany to U.S.A.
Quantity: Over 20 available
Stock Image
Data Construction Method for Small Sample Sets
Published by
VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2010
ISBN 10: 3838398378
ISBN 13: 9783838398372
New
Softcover
Seller: Books Puddle, New York, NY, U.S.A.
Seller rating 4 out of 5 stars
Condition: New. pp. 172. Seller Inventory # 26128886500
Stock Image
Data Construction Method for Small Sample Sets
Published by
VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2010
ISBN 10: 3838398378
ISBN 13: 9783838398372
New
Softcover
Seller: Majestic Books, Hounslow, United Kingdom
Seller rating 4 out of 5 stars
Condition: New. Print on Demand pp. 172 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam. Seller Inventory # 131668283
Buy New
£ 91.96
£ 6.50 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: 4 available
Stock Image
Data Construction Method for Small Sample Sets
Published by
VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2010
ISBN 10: 3838398378
ISBN 13: 9783838398372
New
Softcover
Seller: Biblios, Frankfurt am main, HESSE, Germany
Seller rating 4 out of 5 stars
Condition: New. PRINT ON DEMAND pp. 172. Seller Inventory # 18128886510
Seller Image
Data Construction Method for Small Sample Sets | Theory and Applications
Published by
LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3838398378
ISBN 13: 9783838398372
New
Taschenbuch
Seller: preigu, Osnabrück, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Data Construction Method for Small Sample Sets | Theory and Applications | Hsiao-Fan Wang (u. a.) | Taschenbuch | 172 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783838398372 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Seller Inventory # 107341511
Seller Image
Data Construction Method for Small Sample Sets
Published by
LAP LAMBERT Academic Publishing Aug 2010, 2010
ISBN 10: 3838398378
ISBN 13: 9783838398372
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Data Construction Method (DCM) based on the multiset division is proposed. The DCM can not only generate addition data within the domain value of the given sample for revealing the data's patterns, but also creates the membership function from the generated data for further applications. In this way, the DCM is taken to filling up the information gaps caused by small-sample-sets. To demonstrate the effectiveness of DCM, after presenting the DCM's theoretic background, properties, and algorithm, we compared the DCM with several existing approaches in estimating the population mean and improving the supervised neural network learning performance. The results show that the DCM performs better in a comparative manner. To show its applicability, we have applied the membership function derived from the DCM data to the studies of predicting the severe earthquakes in Taiwan and forecasting the psychotic episode of individual schizophrenics. The results have shown that the DCM can provide appropriate references for prediction from both spatial and temporal small data sets.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 172 pp. Englisch. Seller Inventory # 9783838398372
Seller Image
Data Construction Method for Small Sample Sets : Theory and Applications
Published by
LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838398378
ISBN 13: 9783838398372
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Data Construction Method (DCM) based on the multiset division is proposed. The DCM can not only generate addition data within the domain value of the given sample for revealing the data's patterns, but also creates the membership function from the generated data for further applications. In this way, the DCM is taken to filling up the information gaps caused by small-sample-sets. To demonstrate the effectiveness of DCM, after presenting the DCM's theoretic background, properties, and algorithm, we compared the DCM with several existing approaches in estimating the population mean and improving the supervised neural network learning performance. The results show that the DCM performs better in a comparative manner. To show its applicability, we have applied the membership function derived from the DCM data to the studies of predicting the severe earthquakes in Taiwan and forecasting the psychotic episode of individual schizophrenics. The results have shown that the DCM can provide appropriate references for prediction from both spatial and temporal small data sets. Seller Inventory # 9783838398372