{"id":375,"date":"2017-11-17T17:00:43","date_gmt":"2017-11-17T16:00:43","guid":{"rendered":"http:\/\/pages.di.unipi.it\/bacciu\/?page_id=375"},"modified":"2017-11-17T17:02:35","modified_gmt":"2017-11-17T16:02:35","slug":"iobhtmm","status":"publish","type":"page","link":"https:\/\/pages.di.unipi.it\/bacciu\/software\/iobhtmm\/","title":{"rendered":"IOBHTMM (Python)"},"content":{"rendered":"<h2><strong>An Input-Output Bottom-up Hidden Tree Markov Model<br \/>\n<\/strong><\/h2>\n<p>The official Python implementation for the Bottom-up Hidden Tree Markov model (BHTMM) in its (more general) input-output version (IOBHTMM). The model learns a distribution over tree-structured data, implemented throughout a generative process acting from the leaves to the root of the tree.<\/p>\n<p>The code is maintained in the Github of my student <a href=\"https:\/\/github.com\/diningphil\">Federico Errica<\/a>, who is to be credited for the implementation. To download the code and the scripts necessary to replicate the experiments in the original paper describing the model, please go <a href=\"https:\/\/github.com\/diningphil\/IOBHTMM\">here<\/a>.<\/p>\n<p>The code is provided as is with no warranty and technical support. Please inform the author (<a href=\"http:\/\/www.di.unipi.it\/%7Ebacciu\">Davide Bacciu<\/a>) if you intend to redistribute the code.<\/p>\n<h2><strong>Citation<\/strong><\/h2>\n<p>If you find this code useful, please remember to cite:<\/p>\n<ol>\n<li><div class=\"tp_single_publication\"><span class=\"tp_single_author\">Bacciu Davide, Micheli Alessio, Sperduti Alessandro: <\/span> <span class=\"tp_single_title\">Compositional Generative Mapping for Tree-Structured Data; Part I: Bottom-Up Probabilistic Modeling of Trees<\/span>. <span class=\"tp_single_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Neural Networks and Learning Systems, IEEE Transactions on, <\/span><span class=\"tp_pub_additional_volume\">vol. 23, <\/span><span class=\"tp_pub_additional_number\">no. 12, <\/span><span class=\"tp_pub_additional_pages\">pp. 1987 -2002, <\/span><span class=\"tp_pub_additional_year\">2012<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 2162-237X<\/span>.<\/span><\/div><\/li>\n<li><div class=\"tp_single_publication\"><span class=\"tp_single_author\">Bacciu Davide, Micheli Alessio, Sperduti Alessandro : <\/span> <span class=\"tp_single_title\">An input\u2013output hidden Markov model for tree transductions<\/span>. <span class=\"tp_single_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Neurocomputing, <\/span><span class=\"tp_pub_additional_volume\">vol. 112, <\/span><span class=\"tp_pub_additional_pages\">pp. 34\u201346, <\/span><span class=\"tp_pub_additional_year\">2013<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 0925-2312<\/span>.<\/span><\/div><\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>An Input-Output Bottom-up Hidden Tree Markov Model The official Python implementation for the Bottom-up Hidden Tree Markov model (BHTMM) in its (more general) input-output version (IOBHTMM). The model learns a distribution over tree-structured data, implemented throughout a generative process acting from the leaves to the root of the tree. The code is maintained in the [&hellip;]<\/p>\n","protected":false},"author":19,"featured_media":0,"parent":63,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-375","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/pages.di.unipi.it\/bacciu\/wp-json\/wp\/v2\/pages\/375","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pages.di.unipi.it\/bacciu\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/pages.di.unipi.it\/bacciu\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/pages.di.unipi.it\/bacciu\/wp-json\/wp\/v2\/users\/19"}],"replies":[{"embeddable":true,"href":"https:\/\/pages.di.unipi.it\/bacciu\/wp-json\/wp\/v2\/comments?post=375"}],"version-history":[{"count":6,"href":"https:\/\/pages.di.unipi.it\/bacciu\/wp-json\/wp\/v2\/pages\/375\/revisions"}],"predecessor-version":[{"id":381,"href":"https:\/\/pages.di.unipi.it\/bacciu\/wp-json\/wp\/v2\/pages\/375\/revisions\/381"}],"up":[{"embeddable":true,"href":"https:\/\/pages.di.unipi.it\/bacciu\/wp-json\/wp\/v2\/pages\/63"}],"wp:attachment":[{"href":"https:\/\/pages.di.unipi.it\/bacciu\/wp-json\/wp\/v2\/media?parent=375"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}