<div><divtypeof='http://schema.org/Dataset'about='#suss_repo'><table><tr><th>ID</th><td><divproperty='http://schema.org/identifier'>suss_repo</div></td></tr><tr><th>Title</th><td><divproperty='http://schema.org/name'>SUSS Repository</div></td></tr><tr><th>Organisation</th><td>Sydney University Speleological Society</td></tr><tr><th>Description</th><td><divproperty='http://schema.org/description'>Caves data repository</div></td></tr></table><h2>Items</h2><table><tr><th>Filename</th><th>Description</th><th>Authors</th><th>License</th><th/></tr>
<trabout='#0e45b3b9-cc65-4a1a-8e1f-45fc937d471e'typeof='http://schema.org/MediaObject'id='0e45b3b9-cc65-4a1a-8e1f-45fc937d471e'><td>cp7glop.svx</td><td><divproperty='http://schema.org/description'>Survex data file for Glop Pot</div></td><td>Phil Maynard</td><td><divproperty='http://schema.org/license'>CC-BY</div></td><tdrev='http://schema.org/hasPart'href='#suss_repo'></td></tr>
<trabout='#6ed61bd4-db23-4902-9fa4-ea1cf85f0540'typeof='http://schema.org/MediaObject'id='6ed61bd4-db23-4902-9fa4-ea1cf85f0540'><td>cp7glop.ai</td><td><divproperty='http://schema.org/description'>Illustrator file for Glop Pot</div></td><td>Phil Maynard</td><td><divproperty='http://schema.org/license'>CC-BY</div></td><tdrev='http://schema.org/hasPart'href='#suss_repo'></td></tr>
<trabout='#e6751c56-42c7-42bb-9c99-84a36b99845a'typeof='http://schema.org/MediaObject'id='e6751c56-42c7-42bb-9c99-84a36b99845a'><td>asf</td><td><divproperty='http://schema.org/description'>Directory of ASF Grade specifications for Survex software</div></td><td></td><td><divproperty='http://schema.org/license'></div></td><tdrev='http://schema.org/hasPart'href='#suss_repo'></td></tr>
<trabout='#e9c00dd0-5e30-4b41-8a5c-7ae0417b2de0'typeof='http://schema.org/MediaObject'id='e9c00dd0-5e30-4b41-8a5c-7ae0417b2de0'><td>datasheets</td><td><divproperty='http://schema.org/description'>Directory of scanned data sheets</div></td><td></td><td><divproperty='http://schema.org/license'></div></td><tdrev='http://schema.org/hasPart'href='#suss_repo'></td></tr>
<trabout='#39030787-1ae2-4637-b90e-d63710ae2920'typeof='http://schema.org/MediaObject'id='39030787-1ae2-4637-b90e-d63710ae2920'><td>sketchsheets</td><td><divproperty='http://schema.org/description'>Directory of scanned sketch sheets</div></td><td></td><td><divproperty='http://schema.org/license'></div></td><tdrev='http://schema.org/hasPart'href='#suss_repo'></td></tr>
"description":"This demo is the sampling inference for Graph Topic Model, and more details about this model can be found in the following reference: @ARTICLE{7015568, author={J. Xuan and J. Lu and G. Zhang and X. Luo}, journal={IEEE Transactions on Cybernetics}, title={Topic Model for Graph Mining}, year={2015}, volume={45}, A Markov chain Monte Carlo (MCMC) algorithm is developed and implemented to inference the Graph Topic Model (GTM). GTM is a probabilistic graphical model for the data represented by graph structure, e.g., chemical formulas or documents.",
"description":"This demo is the sampling inference for Graph Topic Model, and more details about this model can be found in the following reference: @ARTICLE{7015568, author={J. Xuan and J. Lu and G. Zhang and X. Luo}, journal={IEEE Transactions on Cybernetics}, title={Topic Model for Graph Mining}, year={2015}, volume={45}, A Markov chain Monte Carlo (MCMC) algorithm is developed and implemented to inference the Graph Topic Model (GTM). GTM is a probabilistic graphical model for the data represented by graph structure, e.g., chemical formulas or documents.",
"description":"GNU GENERAL PUBLIC LICENSE\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\n\r\r\nVersion 3, 29 June 2007",
"This demo is the sampling inference for Graph Topic Model, and more details about this model can be found in the following reference: @ARTICLE{7015568, author={J. Xuan and J. Lu and G. Zhang and X. Luo}, journal={IEEE Transactions on Cybernetics}, title={Topic Model for Graph Mining}, year={2015}, volume={45}, A Markov chain Monte Carlo (MCMC) algorithm is developed and implemented to inference the Graph Topic Model (GTM). GTM is a probabilistic graphical model for the data represented by graph structure, e.g., chemical formulas or documents."
