• 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 15 matches

• 2024
• 
  Miguel Da Corte, Jorge Baptista:
  Leveraging NLP and Machine Learning for English (L1) Writing Assessment in Developmental Education. CSEDU (2) 2024: 128-140
• 2023
• 
  Georgios P. Georgiou, Elena Theodorou:
  Detection of developmental language disorder in Cypriot Greek children using a machine learning neural network algorithm. CoRR abs/2311.15054 (2023)
• 2022
• 
  Rémy Portelas:
  Automatic Curriculum Learning for Developmental Machine Learners. (Génération automatique de curriculum pour apprenants artificiels). University of Bordeaux, France, 2022
• 
  Liam Barrett, Junchao Hu, Peter Howell:
  Systematic Review of Machine Learning Approaches for Detecting Developmental Stuttering. IEEE ACM Trans. Audio Speech Lang. Process. 30: 1160-1172 (2022)
• 
  James M. Rehg, Pierre-Yves Oudeyer, Linda B. Smith, Sho Tsuji, Stefan Stojanov, Ngoc Anh Thai:
  Developmental Machine Learning: From Human Learning to Machines and Back (Dagstuhl Seminar 22422). Dagstuhl Reports 12(10): 143-165 (2022)
• 2021
• 
  Peter Raatikainen, Jarkko Hautala, Otto Loberg, Tommi Kärkkäinen, Paavo H. T. Leppänen, Paavo Nieminen:
  Detection of developmental dyslexia with machine learning using eye movement data. Array 12: 100087 (2021)
• 
  Ricardo Buettner, Michael Buechele, Benedikt Grimmeisen, Patrick Ulrich:
  Machine Learning Based Diagnostics of Developmental Coordination Disorder using Electroencephalographic Data. HICSS 2021: 1-10
• 
  Johannes Breitenbach, Dominik Raab, Eric Fezer, Daniel Sauter, Hermann Baumgartl, Ricardo Buettner:
  Automatic Diagnosis of Intellectual and Developmental Disorder Using Machine Learning Based on Resting-State EEG Recordings. WiMob 2021: 7-12
• 2020
• 
  Shahriar Kaisar:
  Developmental dyslexia detection using machine learning techniques : A survey. ICT Express 6(3): 181-184 (2020)
• 
  Pierre-Yves Oudyer:
  Developmental machine learning. EGC 2020: 3-4
• 
  János Szalma, Kathleen Kay Amora, Zoltán Vidnyánszky, Béla Weiss:
  Investigating the Effect of Inter-letter Spacing Modulation on Data-Driven Detection of Developmental Dyslexia Based on Eye-Movement Correlates of Reading: A Machine Learning Approach. ICPR Workshops (3) 2020: 467-481
• 2019
• 
  Finn Kuusisto, Vítor Santos Costa, Zhonggang Hou, James A. Thomson, David Page, Ron M. Stewart:
  Machine Learning to Predict Developmental Neurotoxicity with High-Throughput Data from 2D Bio-Engineered Tissues. ICMLA 2019: 293-298
• 
  Finn Kuusisto, Vítor Santos Costa, Zhonggang Hou, James A. Thomson, David Page, Ron M. Stewart:
  Machine Learning to Predict Developmental Neurotoxicity with High-throughput Data from 2D Bio-engineered Tissues. CoRR abs/1905.02121 (2019)
• 2018
• 
  Alisha Kamat, Ting Jin, So Yeon Min, Flaminia Talos, Jonas S. Almeida, Daifeng Wang:
  Interpretable Machine Learning Approach Reveals Developmental Gene Expression Biomarkers for Cancer Patient Outcomes at Early Stages. BCB 2018: 510
• 2017
• 
  Daniel Camilleri, Tony J. Prescott:
  Analysing the Limitations of Deep Learning for Developmental Robotics. Living Machines 2017: 86-94