Unsupervised Learning, Clustering, and Self-Organization
Unsupervised learning does not require annotation or labeling from a human teacher; the idea is to learn the structure of the data from unlabeled examples. The most common unsupervised learning task is clustering, i.e. grouping instances into a discovered set of categories containing similar instances. Self-organizing maps in addition visualize the topology of the clusters on a map. Our work in this area includes applications on lexical semantics, topic modeling, and discovering latent class models, as well as methods for laterally connected, hierarchical, sequential-input, and growing self-organizing maps.
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Infinite-Word Topic Models for Digital Media Austin Waters PhD Thesis, Department of Computer Science, The University of Texas at Austin, 2014. 2014

Latent Class Models for Algorithm Portfolio Methods Bryan Silverthorn and Risto Miikkulainen In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, 2010. 2010

Spherical Topic Models Joseph Reisinger, Austin Waters, Bryan Silverthorn, and Raymond J. Mooney In Proceedings of the 27th International Conference on Machine Learning (ICML 2010), 2010. 2010

Spherical Topic Models Joseph Reisinger, Austin Waters, Bryan Silverthorn, and Raymond Mooney In NIPS'09 workshop: Applications for Topic Models: Text and Beyond, 2009. 2009

Temporal Convolution Machines for Sequence Learning Alan J Lockett and Risto Miikkulainen Technical Report AI-09-04, Department of Computer Sciences, the University of Texas at Austin, 2009. 2009

Self-Organizing Distinctive State Abstraction Using Options Jefferson Provost, Benjamin J. Kuipers, and Risto Miikkulainen In Proceedings of the 7th International Conference on Epigenetic Robotics, 2007. 2007

Self-Organization of Hierarchical Visual Maps with Feedback Connections Yiu Fai Sit and Risto Miikkulainen Neurocomputing, 69:1309-1312, 2006. 2006

Laterally Interconnected Self-Organizing Maps In Hand-Written Digit Recognition Yoonsuck Choe, Joseph Sirosh, and Risto Miikkulainen In David S. Touretzky and Michael C. Mozer and Michael E. Hasselmo, editors, Advances in Neural ... 1996

Lateral Interactions In The Cortex: Structure And Function Joseph Sirosh, Risto Miikkulainen, and Yoonsuck Choe (editors) Electronic book, ISBN 0-9647060-0-8, http://nn.cs.utexas.edu/web-pubs/htmlbook96/. Austin, TX: The U... 1996

Introduction: The Emerging Understanding of Lateral Interactions in the Cortex Risto Miikkulainen and Joseph Sirosh In Sirosh, J., Miikkulainen, R., and Choe, Y., editors, Lateral Interactions in the Cortex: Struc... 1996

Visualizing High-Dimensional Structure With The Incremental Grid Growing Neural Network Justine Blackmore and Risto Miikkulainen In Armand Prieditis and Stuart Russell, editors, Machine Learning: Proceedings of the 12th Annual... 1995

Laterally Interconnected Self-Organizing Feature Map In Handwritten Digit Recognition Yoonsuck Choe Masters Thesis, Department of Computer Sciences, The University of Texas at Austin, Austin, TX, 1995... 1995

Visualizing High-Dimensional Structure with the Incremental Grid Growing Network Justine Blackmore Masters Thesis, Department of Computer Sciences, The University of Texas at Austin, Austin, TX, 1995... 1995

SARDNET: A Self-Organizing Feature Map For Sequences Daniel L. James and Risto Miikkulainen In G. Tesauro, D. S. Touretzky, and T. K. Leen, editors, Advances in Neural Information Processin... 1995

Incremental Grid Growing: Encoding High-Dimensional Structure Into A Two-Dimensional Feature Map Justine Blackmore and Risto Miikkulainen In Proceedings of the IEEE International Conference on Neural Networks (San Francisco, CA), 4... 1993

How Lateral Interaction Develops In A Self-Organizing Feature Map Joseph Sirosh and Risto Miikkulainen In Proceedings of the IEEE International Conference on Neural Networks (San Francisco, CA), 1... 1993

Self-Organizing Process Based On Lateral Inhibition And Synaptic Resource Redistribution Risto Miikkulainen In Teuvo Kohonen and Kai M{"a}kisara and Olli Simula and Jari Kangas, editors, Proceedings of the... 1991

Script Recognition With Hierarchical Feature Maps Risto Miikkulainen Connection Science, 2:83-101, 1990. 1990

Justine Blackmore Masters Alumni jblackmorehlista [at] yahoo com
Yoonsuck Choe Ph.D. Alumni choe [at] tamu edu
Daniel L. James Undergraduate Alumni
Vito Ruiz Masters Alumni
Bryan Silverthorn Ph.D. Alumni bsilvert [at] cs utexas edu
Joseph Sirosh Ph.D. Alumni joseph sirosh [at] gmail com
Yiu Fai Sit Ph.D. Alumni yfsit [at] cs utexas edu
Austin Waters Ph.D. Student austin [at] cs utexas edu
SOFM The SOFM package contains C- and TK/TCL-code (integrated through SWIG) for the standard feature map algorithm for formi... 2002

LISSOM

The LISSOM package contains the C++, Python, and Scheme source code and examples for training and testing firing-rate...

2001

HFM The HFM package contains the C-code and data for training and testing the HFM memory organization and hierarchical class... 1994