{"id":148455,"date":"1999-02-01T00:00:00","date_gmt":"1999-02-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/fast-learning-from-sparse-data\/"},"modified":"2018-10-16T21:18:32","modified_gmt":"2018-10-17T04:18:32","slug":"fast-learning-from-sparse-data","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/fast-learning-from-sparse-data\/","title":{"rendered":"Fast Learning from Sparse Data"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We describe two techniques that significantly improve the running time of several standard machine-learning algorithms when data is sparse. The first technique is an algorithm that efficiently extracts one-way and two-way counts \u2014 either real or expected \u2014 from discrete data. Extracting such counts is a fundamental step in learning algorithm for constructing a variety of models including decision trees, decision graphs, Bayesian networks, and naive-Bayes clustering models. The second technique is an algorithm that efficiently performs the E-step of the EM algorithm (i.e., inference) when applied to a naive-Bayes clustering model. Using real-world data sets, we demonstrate a dramatic decrease in running time for algorithms that incorporate these techniques.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We describe two techniques that significantly improve the running time of several standard machine-learning algorithms when data is sparse. The first technique is an algorithm that efficiently extracts one-way and two-way counts \u2014 either real or expected \u2014 from discrete data. Extracting such counts is a fundamental step in learning algorithm for constructing a variety [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13561,13556],"msr-publication-type":[193718],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-148455","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-algorithms","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"Morgan Kaufmann","msr_edition":"Proceedings of Fifteenth Conference on Uncertainty in Artificial 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