• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 32 matches

• 2021
• 
  Yu-Shiang Lin, Pei-Hsin Huang, Yung-Yaw Chen:
  Deep Learning-Based Hepatocellular Carcinoma Histopathology Image Classification: Accuracy Versus Training Dataset Size. IEEE Access 9: 33144-33157 (2021)
• 
  Chun Li, Yunyun Yang, Hui Liang, Boying Wu:
  Transfer learning for establishment of recognition of COVID-19 on CT imaging using small-sized training datasets. Knowl. Based Syst. 218: 106849 (2021)
• 
  Christopher A. Ramezan, Timothy A. Warner, Aaron E. Maxwell, Bradley S. Price:
  Effects of Training Set Size on Supervised Machine-Learning Land-Cover Classification of Large-Area High-Resolution Remotely Sensed Data. Remote. Sens. 13(3): 368 (2021)
• 2020
• 
  Huan Ning, Zhenlong Li, Cuizhen Wang, Lina Yang:
  Choosing an appropriate training set size when using existing data to train neural networks for land cover segmentation. Ann. GIS 26(4): 329-342 (2020)
• 
  Nicole Dalia Cilia, Claudio De Stefano, Francesco Fontanella, Mario Molinara, Alessandra Scotto di Freca:
  What is the minimum training data size to reliably identify writers in medieval manuscripts? Pattern Recognit. Lett. 129: 198-204 (2020)
• 
  Wanwan Zheng, Mingzhe Jin:
  The Effects of Class Imbalance and Training Data Size on Classifier Learning: An Empirical Study. SN Comput. Sci. 1(2): 71 (2020)
• 
  Harumo Sasatake, Ryosuke Tasaki, Naoki Uchiyama:
  Deep Imitation Learning for Broom-Manipulation Tasks Using Small-Sized Training Data. CoDIT 2020: 733-738
• 
  Louis Martin, Benjamin Müller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Benoît Sagot, Djamé Seddah:
  Les modèles de langue contextuels Camembert pour le français : impact de la taille et de l'hétérogénéité des données d'entrainement (C AMEM BERT Contextual Language Models for French: Impact of Training Data Size and Heterogeneity ). JEP-TALN-RECITAL (2) 2020: 54-65
• 2019
• 
  Lei Zhang:
  Evaluating the Effects of Size and Precision of Training Data on ANN Training Performance for the Prediction of Chaotic Time Series Patterns. Int. J. Softw. Sci. Comput. Intell. 11(1): 16-30 (2019)
• 
  Taiwo Oyedare, Jung-Min Jerry Park:
  Estimating the Required Training Dataset Size for Transmitter Classification Using Deep Learning. DySPAN 2019: 1-10
• 
  Trond Linjordet, Krisztian Balog:
  Impact of Training Dataset Size on Neural Answer Selection Models. ECIR (1) 2019: 828-835
• 
  John Hale, Adhiguna Kuncoro, Keith Hall, Chris Dyer, Jonathan Brennan:
  Text Genre and Training Data Size in Human-like Parsing. EMNLP/IJCNLP (1) 2019: 5845-5851
• 
  Sandro Pezzelle, Raquel Fernández:
  Big Generalizations with Small Data: Exploring the Role of Training Samples in Learning Adjectives of Size. LANTERN@EMNLP-IJCNLP 2019: 18-23
• 
  Robert Rehr, Timo Gerkmann:
  An Analysis of Noise-aware Features in Combination with the Size and Diversity of Training Data for DNN-based Speech Enhancement. ICASSP 2019: 601-605
• 
  Trond Linjordet, Krisztian Balog:
  Impact of Training Dataset Size on Neural Answer Selection Models. CoRR abs/1901.10496 (2019)
• 
  Talha Cihad Gulcu:
  Stronger Convergence Results for Deep Residual Networks: Network Width Scales Linearly with Training Data Size. CoRR abs/1911.04351 (2019)
• 2018
• 
  Yuta Hiasa, Yoshito Otake, Masaki Takao, Takumi Matsuoka, Kazuma Takashima, Aaron Carass, Jerry L. Prince, Nobuhiko Sugano, Yoshinobu Sato:
  Cross-Modality Image Synthesis from Unpaired Data Using CycleGAN - Effects of Gradient Consistency Loss and Training Data Size. SASHIMI@MICCAI 2018: 31-41
• 
  Yuta Hiasa, Yoshito Otake, Masaki Takao, Takumi Matsuoka, Kazuma Takashima, Jerry L. Prince, Nobuhiko Sugano, Yoshinobu Sato:
  Cross-modality image synthesis from unpaired data using CycleGAN: Effects of gradient consistency loss and training data size. CoRR abs/1803.06629 (2018)
• 2017
• 
  Jinya Su, Dewei Yi, Cunjia Liu, Lei Guo, Wen-Hua Chen:
  Dimension Reduction Aided Hyperspectral Image Classification with a Small-sized Training Dataset: Experimental Comparisons. Sensors 17(12): 2726 (2017)
• 
  Stefanos Ougiaroglou, Georgios Arampatzis, Dimitris A. Dervos, Georgios Evangelidis:
  Generating Fixed-Size Training Sets for Large and Streaming Datasets. ADBIS 2017: 88-102
• 2015
• 
  Joseph D. Prusa, Taghi M. Khoshgoftaar, Naeem Seliya:
  The Effect of Dataset Size on Training Tweet Sentiment Classifiers. ICMLA 2015: 96-102
• 2014
• 
  Waleed A. Yousef, Subrata Kundu:
  Learning algorithms may perform worse with increasing training set size: Algorithm-data incompatibility. Comput. Stat. Data Anal. 74: 181-197 (2014)
• 
  Ismael Fernández, Fernando J. Aguilar, Manuel A. Aguilar, Flor Alvarez:
  Influence of Data Source and Training Size on Impervious Surface Areas Classification Using VHR Satellite and Aerial Imagery Through an Object-Based Approach. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 7(12): 4681-4691 (2014)
• 2013
• 
  Erel Joffe, Michael J. Byrne, Phillip Reeder, Jorge R. Herskovic, Craig W. Johnson, Allison B. McCoy, Elmer V. Bernstam:
  Optimized Dual Threshold Entity Resolution For Electronic Health Record Databases - Training Set Size And Active Learning. AMIA 2013
• 
  William Lewis, Sauleh Eetemadi:
  Dramatically Reducing Training Data Size Through Vocabulary Saturation. WMT@ACL 2013: 281-291
• 2012
• 
  Lan Du, Hongwei Liu, Penghui Wang, Bo Feng, Mian Pan, Zheng Bao:
  Noise Robust Radar HRRP Target Recognition Based on Multitask Factor Analysis With Small Training Data Size. IEEE Trans. Signal Process. 60(7): 3546-3559 (2012)
• 2011
• 
  Pankaj Kumar, Xiao Hua Ma, Xianghui Liu, Jia Jia, Bu-Cong Han, Ying Xue, Ze-Rong Li, Sheng-Yong Yang, Yu-Quan Wei, Yu Zong Chen:
  Effect of training data size and noise level on support vector machines virtual screening of genotoxic compounds from large compound libraries. J. Comput. Aided Mol. Des. 25(5): 455-467 (2011)
• 2007
• 
  Georgios Lappas:
  Estimating the Size of Neural Networks from the Number of Available Training Data. ICANN (1) 2007: 68-77
• 2005
• 
  Joan-Andreu Sánchez, José-Miguel Benedí, Diego Linares:
  Performance of a SCFG-Based Language Model with Training Data Sets of Increasing Size. IbPRIA (2) 2005: 586-594
• 2001
• 
  Yoshihiko Muto, Yoshihiko Hamamoto:
  Improvement of the Parzen classifier in small training sample size situations. Intell. Data Anal. 5(6): 477-490 (2001)

skipping 2 more matches