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There are 9002 results for: content related to: Next-Day Peak Electricity Price Forecasting Using NN Based on Rough Sets Theory

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    Spatial and temporal multiyear sea ice distributions in the Arctic: A neural network analysis of SSM/I data, 1988–2001

    Journal of Geophysical Research: Oceans (1978–2012)

    Volume 109, Issue C10, October 2004, Gennady I. Belchansky, David C. Douglas, Ilia V. Alpatsky and Nikita G. Platonov

    Version of Record online : 30 OCT 2004, DOI: 10.1029/2004JC002388

  2. Robust Face Detection Using One-Class Estimation and Real AdaBoost

    Electronics and Communications in Japan

    Volume 97, Issue 7, July 2014, Pages: 39–47, Shotaro Miwa, Takashi Hirai and Kazuhiko Sumi

    Version of Record online : 9 JUN 2014, DOI: 10.1002/ecj.11564

  3. Multilevel factor analytic models for assessing the relationship between nurse-reported adverse events and patient safety

    Journal of the Royal Statistical Society: Series A (Statistics in Society)

    Volume 177, Issue 1, January 2014, Pages: 237–257, Luwis Diya, Baoyue Li, Koen Van den Heede, Walter Sermeus and Emmanuel Lesaffre

    Version of Record online : 23 APR 2013, DOI: 10.1111/rssa.12012

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    Automatic classification of chromosomes as part of a routine system for clinical analysis

    Cytometry

    Volume 7, Issue 1, January 1986, Pages: 1–7, Claes Lundsteen, Tommy Gerdes and Jan Maahr

    Version of Record online : 8 MAR 2005, DOI: 10.1002/cyto.990070102

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    Simulation Models of the Influence of Learning Mode and Training Variance on Category Learning

    Cognitive Science

    Volume 18, Issue 2, April 1994, Pages: 185–219, Renée Elio and Kui Lin

    Version of Record online : 11 FEB 2010, DOI: 10.1207/s15516709cog1802_1

  6. Mean squared error of prediction (MSEP) estimates for principal component regression (PCR) and partial least squares regression (PLSR)

    Journal of Chemometrics

    Volume 18, Issue 9, September 2004, Pages: 422–429, Bjørn-Helge Mevik and Henrik René Cederkvist

    Version of Record online : 11 MAR 2005, DOI: 10.1002/cem.887

  7. Statistical Models for Analyzing Learning Data

    New Directions for Teaching and Learning

    Volume 2013, Issue 136, Winter 2013, Pages: 49–58, Georjeanna Wilson-Doenges

    Version of Record online : 19 DEC 2013, DOI: 10.1002/tl.20075

  8. Impacts on type I error rate with inappropriate use of learn and confirm in confirmatory adaptive design trials

    Biometrical Journal

    Volume 52, Issue 6, December 2010, Pages: 798–810, Sue-Jane Wang, H. M. James Hung and Robert T. O'Neill

    Version of Record online : 13 DEC 2010, DOI: 10.1002/bimj.200900207

  9. Part of speech tagging with min-max modular neural networks

    Systems and Computers in Japan

    Volume 33, Issue 7, 30 June 2002, Pages: 30–39, Qing Ma, Bao-Liang Lu, Hitoshi Isahara and Michinori Ichikawa

    Version of Record online : 25 APR 2002, DOI: 10.1002/scj.1139

  10. On-line supervising mechanism for learning data in surface electromyogram motion classifiers

    Systems and Computers in Japan

    Volume 33, Issue 14, December 2002, Pages: 1–11, Daisuke Nishikawa, Wenwei Yu, Hiroshi Yokoi and Yukinori Kakazu

    Version of Record online : 23 OCT 2002, DOI: 10.1002/scj.10245

  11. A new recursive neural network algorithm to forecast electricity price for PJM day-ahead market

    International Journal of Energy Research

    Volume 34, Issue 6, May 2010, Pages: 507–522, Paras Mandal, Anurag K. Srivastava, Tomonobu Senjyu and Michael Negnevitsky

    Version of Record online : 22 JUN 2009, DOI: 10.1002/er.1569

  12. Daily electric load forecasting using artificial neural network

    Electrical Engineering in Japan

    Volume 115, Issue 6, October 1995, Pages: 52–61, Takaharu Ishida and Shigeru Tamura

    Version of Record online : 22 MAR 2007, DOI: 10.1002/eej.4391150606

  13. Use of Leave-in-Place Sensors and SHM Methods to Improve Assessments of Aging Structures

    Standard Article

    Encyclopedia of Structural Health Monitoring

    Dennis Roach

    Published Online : 15 SEP 2009, DOI: 10.1002/9780470061626.shm197

  14. Sensitivity analysis of neural network parameters to improve the performance of electricity price forecasting

    International Journal of Energy Research

    Volume 33, Issue 1, January 2009, Pages: 38–51, Paras Mandal, Anurag K. Srivastava, Michael Negnevitsky and Jung-Wook Park

    Version of Record online : 30 SEP 2008, DOI: 10.1002/er.1469

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    Tree-based reinforcement learning for optimal water reservoir operation

    Water Resources Research

    Volume 46, Issue 9, September 2010, A. Castelletti, S. Galelli, M. Restelli and R. Soncini-Sessa

    Version of Record online : 3 SEP 2010, DOI: 10.1029/2009WR008898

  16. Reconstructing clusters for preconditioned short-term load forecasting

    Electrical Engineering in Japan

    Volume 161, Issue 1, October 2007, Pages: 26–33, Tadahiro Itagaki and Hiroyuki Mori

    Version of Record online : 21 JUN 2007, DOI: 10.1002/eej.20300

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    Beyond performance data: Improving student help seeking by collecting and displaying influential data in an online middle-school science curriculum

    British Journal of Educational Technology

    Volume 47, Issue 1, January 2016, Pages: 121–134, Samantha G. Daley, Garron Hillaire and LeeAnn M. Sutherland

    Version of Record online : 15 OCT 2014, DOI: 10.1111/bjet.12221

  18. IS THE SERIAL-POSITION CURVE INVARIANT?

    British Journal of Psychology

    Volume 53, Issue 2, May 1962, Pages: 159–166, ARTHUR R. JENSEN

    Version of Record online : 13 APR 2011, DOI: 10.1111/j.2044-8295.1962.tb00822.x

  19. Measuring Reaction and Learning

    Project Management ROI: A Step-by-Step Guide for Measuring the Impact and ROI for Projects

    Jack J. Phillips, Wayne Brantley, Patricia Pulliam Phillips, Pages: 79–94, 2011

    Published Online : 26 OCT 2011, DOI: 10.1002/9781118122587.ch5

  20. Measuring Reaction and Learning

    Measuring ROI in Environment, Health, and Safety

    Jack J. Phillips, Patricia Phillips, Al Pulliam, Pages: 135–150, 2014

    Published Online : 3 JAN 2014, DOI: 10.1002/9781118899281.ch6