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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 11 of 11 1 Recurrent Autoassociative Networks and Holistic Computations Ivelin Stoianov In: http://odur.let.rug.nl/~stoianov/ps/ijcnn2000.Stoianov.RAN_Holistic_Computations.ps.gz (2000) BASE Show details 2 Connectionist Learning to Read Aloud and Correlation to Human Data Ivelin Stoianov; Laurie Stowe; John Nerbonne In: http://www.let.rug.nl/~stoianov/ps/CS99_Reading_Stoianov_Stowe_Nerbonne.ps.gz (1999) BASE Show details 3 Connectionist learning to read aloud and correlation to human data Ivelin Stoianov; Laurie Stowe; John Nerbonne In: http://odur.let.rug.nl/~nerbonne/papers/cogsci99.pdf (1999) BASE Show details 4 Connectionist Grapheme to Phoneme Conversion: Exploring Distributed Representations Ivelin Stoianov And; Ivelin Stoianov; John Nerbonne In: http://www-uilots.let.uu.nl/publications/clin1999/Pap/stoianov.ps (1999) BASE Show details 5 Recurrent Autoassociative Networks and Sequential Processing Ivelin Stoianov In: http://www.let.rug.nl/~stoianov/ps/ijcnn99.Stoianov.ps.gz (1999) BASE Show details 6 Connectionist Learning of Natural Language Lexical Phonotactics Ivelin Stoianov; John Nerbonne In: http://www.let.rug.nl/~stoianov/ps/ml.conn.learn.nl.phonotactics.ps.gz (1998) Abstract: Connectionist learning of natural language words and their phonetic regularities is presented. The Neural Network (NN) model we employ in this problem is the Simple Recurrent Network, trained with the Backpropagation Through Time (BPTT) learning algorithm. During the training, it was assigned the task of predicting the next phoneme given one phoneme at each moment and keeping information of the past phonemes from a given word in a few context neurons. The phonotactics of the Dutch language was studied among others. The shortcomings of some similar previous implementations are explained and successfully overcome. Among the techniques we employed to achieve the much-improved error rate of 1.1% with monosyllabic words and 3.5% with multisyllabic ones are new methods for network response interpretation, an evolutionary approach in training a set of networks, and the exploitation of the word frequencies in training. Finally, an analysis of the phonotactics rules extracted by a . Keyword: 2; connectionism; linguistics; machine learning; neural networks; phonotactics; SRN URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.38.1582 http://www.let.rug.nl/~stoianov/ps/ml.conn.learn.nl.phonotactics.ps.gz BASE Hide details 7 Exploring Phonotactics with Simple Recurrent Networks Ivelin Stoianov; John Nerbonne In: http://www.let.rug.nl/~nerbonne/papers/clin98srn.ps (1998) BASE Show details 8 Modelling the phonotactic structure of natural language words with Simple Recurrent Networks Ivelin Stoianov; John Nerbonne; Huub Bouma In: http://grid.let.rug.nl/~nerbonne/papers/clin97.Stoianov.Nerbonne.Bouma.PhonotacticsLearning.pdf (1998) BASE Show details 9 Modelling the phonotactic structure of natural language words with Simple Recurrent Networks Ivelin Stoianov; Huub Bouma; John Nerbonne In: http://grid.let.rug.nl/~stoianov/ps/dutch.phonot.poster97.ps.gz (1997) BASE Show details 10 Modelling the phonotactic structure of natural language words with Simple Recurrent Networks Ivelin Stoianov; John Nerbonne; Huub Bouma In: http://www.let.rug.nl/~stoianov/ps/clin97.Stoianov.Nerbonne.Bouma.PhonotacticsLearning.ps.gz (1997) BASE Show details 11 Tree based Analysis of Simple Recurrent Network Learning Ivelin Stoianov BASE Show details

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