Hierarchical voting experts: An unsupervised algorithm for hierarchical sequence segmentation

No abstract is available for this paper.

• Publication year

  2008

• Venue

  2008 7th IEEE International Conference on Development and Learning

• Publication date

  2008-10-10

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1109/DEVLRN.2008.4640827
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Voting experts: An unsupervised algorithm for segmenting sequences

  2007influential reference
• Contextual Dependencies in Unsupervised Word Segmentation

  2006cited by this paper
• Encoding multielement scenes: statistical learning of visual feature hierarchies.

  2005cited by this paper
• The MARKOV EXPERT for finding episodes in time series

  2005cited by this paper
• Statistical clustering and the contents of the infant vocabulary.

  2005cited by this paper
• Unsupervised Segmentation of Words Using Prior Distributions of Morph Length and Frequency

  2003cited by this paper
• Statistical learning of higher-order temporal structure from visual shape sequences.

  2002cited by this paper
• Experiments in Unsupervised Entropy-Based Corpus Segmentation

  1999cited by this paper
• Statistical learning of tone sequences by human infants and adults.

  1999influential reference
• An Efficient, Probabilistically Sound Algorithm for Segmentation and Word Discovery

  1999cited by this paper
• Identifying Hierarchical Structure in Sequences: A linear-time algorithm

  1997cited by this paper
• Statistical Learning by 8-Month-Old Infants

  1996cited by this paper
• Parsing a Natural Language Using Mutual Information Statistics

  1990cited by this paper
• Word segmentation by letter successor varieties

  1974cited by this paper
• The magical number seven plus or minus two: some limits on our capacity for processing information.

  1956cited by this paper
• Prediction and Entropy of Printed English

  1951cited by this paper
• PSYCHOLOGICAL SCIENCE Research Article UNSUPERVISED STATISTICAL LEARNING OF HIGHER-ORDER SPATIAL STRUCTURES FROM VISUAL SCENES

  year unknowncited by this paper

• Unsupervised Hierarchical Temporal Abstraction by Simultaneously Learning Expectations and Representations

  2019cites this paper
• Adapting Case-Based Reasoning for Processing Natural Phenomena Data

  2015cites this paper
• Unsupervised segmentation of sequences using harmony search and hierarchical clustering techniques

  2014influential citation
• Searching and indexing distorted data collections

  2014cites this paper
• Mining Time Series Data: Moving from Toy Problems to Realistic Deployments

  2013cites this paper
• Unsupervised Algorithm for Post-Processing of Roughly Segmented Categorical Time Series

  2012cites this paper
• Autonomous motion learning for near optimal control

  2012cites this paper
• Unsupervised algorithm for retrieving characteristic patterns from time-warped data collections

  2012cites this paper
• Fully Unsupervised Word Segmentation with BVE and MDL

  2011influential citation
• Word Segmentation as General Chunking

  2011cites this paper
• Czech Text Segmentation Using Voting Experts and Its Comparison with Menzerath-Altmann law

  2011cites this paper
• Artificial General Segmentation

  2010cites this paper
• Biomimetic learning, not learning biomimetics: A survey of developmental learning

  2010cites this paper
• An unsupervised model of infant acoustic speech segmentation

  2009influential citation
• Unsupervised Segmentation of Audio Speech Using the Voting Experts Algorithm Table of Contents List of Tables

  2009cites this paper
• Unsupervised Segmentation of Audio Speech Using the Voting Experts Algorithm

  2009cites this paper
• Bootstrap Voting Experts

  2009influential citation
• Unsupervised Audio Speech Segmentation Using the Voting Experts Algorithm

  2008cites this paper